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Annual vaccination is widely recommended for influenza and SARS‐CoV‐2. In this essay, we analyse and question the prevailing policymaking approach to these respiratory virus vaccines, especially in the United States. Every year, licensed influenza vaccines are reformulated to include specific strains expected to dominate in the season ahead. Updated vaccines are rapidly manufactured and approved without further regulatory requirement of clinical data. Novel vaccines (i.e. new products) typically undergo clinical trials, though generally powered for clinically unimportant outcomes (e.g. lab‐confirmed infections, regardless of symptomatology or antibody levels). Eventually, the current and future efficacy of influenza and COVID‐19 vaccines against hospitalization or death carries considerable uncertainty. The emergence of highly transmissible SARS‐CoV‐2 variants and waning vaccine‐induced immunity led to plummeting vaccine effectiveness, at least against symptomatic infection, and booster doses have since been widely recommended. No further randomized trials were performed for clinically important outcomes for licensed updated boosters. In both cases, annual vaccine effectiveness estimates are generated by observational research, but observational studies are particularly susceptible to confounding and bias. Well‐conducted experimental studies, particularly randomized trials, are necessary to address persistent uncertainties about influenza and COVID‐19 vaccines. We propose a new research framework which would render results relevant to the current or future respiratory viral seasons. We demonstrate that experimental studies are feasible by adopting a more pragmatic approach and provide strategies on how to do so. When it comes to implementing policies that seriously impact people's lives, require substantial public resources and/or rely on widespread public acceptance, high evidence standards are desirable. [ABSTRACT FROM AUTHOR]

Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers. [ABSTRACT FROM AUTHOR]

Qualitative assessments of researchers are resource-intensive, untenable in nonmeritocratic settings, and error-prone. Although often derided, quantitative metrics could help improve research practices if they are rigorous, field-adjusted, and centralized. Using quantitative metrics to assess researchers is often seen as a poor choice compared with using qualitative assessments. In this Perspective, the authors argue in favor of using rigorous, field-adjusted, centralized, quantitative metrics in a bid to help improve research practices as a low-cost public good. [ABSTRACT FROM AUTHOR]

We evaluated how the gender composition of top-cited authors within different subfields of research has evolved over time. We considered 9,071,122 authors with at least 5 full papers in Scopus as of September 1, 2022. Using a previously validated composite citation indicator, we identified the 2% top-cited authors for each of 174 science subfields (Science-Metrix classification) in 4 separate publication age cohorts (first publication pre-1992, 1992 to 2001, 2002 to 2011, and post-2011). Using NamSor, we assigned 3,784,507 authors as men and 2,011,616 as women (for 36.1% gender assignment uncertain). Men outnumbered women 1.88-fold among all authors, decreasing from 3.93-fold to 1.36-fold over time. Men outnumbered women 3.21-fold among top-cited authors, decreasing from 6.41-fold to 2.28-fold over time. In the youngest (post-2011) cohort, 32/174 (18%) subfields had > = 50% women, 97/174 (56%) subfields had > = 30% women, and 3 subfields had = <10% women among the top-cited authors. Gender imbalances in author numbers decreased sharply over time in both high-income countries (including the United States of America) and other countries, but the latter had little improvement in gender imbalances for top-cited authors. In random samples of 100 women and 100 men from the youngest (post-2011) cohort, in-depth assessment showed that most were currently (April 2023) working in academic environments. 32 women and 44 men had some faculty appointment, but only 2 women and 2 men were full professors. Our analysis shows large heterogeneity across scientific disciplines in the amelioration of gender imbalances with more prominent imbalances persisting among top-cited authors and slow promotion pathways even for the most-cited young scientists. An evaluation of the top-cited scientists across science shows a wide range in the amelioration of gender imbalances across scientific disciplines; many scientific disciplines still have fewer women among the top-cited scientists, even among the youngest age cohort. [ABSTRACT FROM AUTHOR]

To make informative public policy decisions in battling the ongoing COVID-19 pandemic, it is important to know the disease prevalence in a population. There are two intertwined difficulties in estimating this prevalence based on testing results from a group of subjects. First, the test is prone to measurement error with unknown sensitivity and specificity. Second, the prevalence tends to be low at the initial stage of the pandemic and we may not be able to determine if a positive test result is a false positive due to the imperfect test specificity. The statistical inference based on a large sample approximation or conventional bootstrap may not be valid in such cases. In this paper, we have proposed a set of confidence intervals, whose validity doesn't depend on the sample size in the unweighted setting. For the weighted setting, the proposed inference is equivalent to hybrid bootstrap methods, whose performance is also more robust than those based on asymptotic approximations. The methods are used to reanalyze data from a study investigating the antibody prevalence in Santa Clara County, California in addition to several other seroprevalence studies. Simulation studies have been conducted to examine the finite-sample performance of the proposed method. [ABSTRACT FROM AUTHOR]

Several teams have been publishing global estimates of excess deaths during the COVID‐19 pandemic. Here, we examine potential flaws and underappreciated sources of uncertainty in global excess death calculations. Adjusting for changing population age structure is essential. Otherwise, excess deaths are markedly overestimated in countries with increasingly aging populations. Adjusting for changes in other high‐risk indicators, such as residence in long‐term facilities, may also make a difference. Death registration is highly incomplete in most countries; completeness corrections should allow for substantial uncertainty and consider that completeness may have changed during pandemic years. Excess death estimates have high sensitivity to modelling choice. Therefore different options should be considered and the full range of results should be shown for different choices of pre‐pandemic reference periods and imposed models. Any post‐modelling corrections in specific countries should be guided by pre‐specified rules. Modelling of all‐cause mortality (ACM) in countries that have ACM data and extrapolating these models to other countries is precarious; models may lack transportability. Existing global excess death estimates underestimate the overall uncertainty that is multiplicative across diverse sources of uncertainty. Informative excess death estimates require risk stratification, including age groups and ethnic/racial strata. Data to‐date suggest a death deficit among children during the pandemic and marked socioeconomic differences in deaths, widening inequalities. Finally, causal explanations require great caution in disentangling SARS‐CoV‐2 deaths, indirect pandemic effects and effects from measures taken. We conclude that excess deaths have many uncertainties, but globally deaths from SARS‐CoV‐2 may be the minority of calculated excess deaths. [ABSTRACT FROM AUTHOR]

The article presents the discussion on generate income from product advertising. Topics include Medical journals have traditionally mainly published advertisements for prescription drugs paid by pharmaceutical companies; and focusing on the financial implications seems to be the main argument for publishing prescription drug advertisements.

Background: China witnessed a surge of Omicron infections after abandoning 'zero COVID' strategies on 7 December 2022. The authorities report very sparse deaths based on very restricted criteria, but massive deaths are speculated. Methods: We aimed to estimate the COVID‐19 fatalities in Mainland China until summer 2023 using the experiences of Hong Kong and of South Korea in 2022 as prototypes. Both these locations experienced massive Omicron waves after having had very few SARS‐CoV‐2 infections during 2020–2021. We estimated age‐stratified infection fatality rates (IFRs) in Hong Kong and South Korea during 2022 and extrapolated to the population age structure of Mainland China. We also accounted separately for deaths of residents in long‐term care facilities in both Hong Kong and South Korea. Results: Infection fatality rate estimates in non‐elderly strata were modestly higher in Hong Kong than South Korea and projected 987,455 and 619,549 maximal COVID‐19 deaths respectively, if the entire China population were infected. Expected COVID‐19 deaths in Mainland China until summer 2023 ranged from 49,962 to 691,219 assuming 25–70% of the non‐elderly population being infected and variable protection of elderly (from none to three‐quarter reduction in fatalities). The main analysis (45% of non‐elderly population infected and fatality impact among elderly reduced by half) estimated 152,886–249,094 COVID‐19 deaths until summer 2023. Large uncertainties exist regarding potential changes in dominant variant, health system strain and impact on non‐COVID‐19 deaths. Conclusions: The most critical factor that can affect total COVID‐19 fatalities in China is the extent to which the elderly can be protected. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID‐19) pandemic has entered its endemic phase and we observe significantly declining infection fatality rates due to severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). On this background, it is crucial but challenging to define current and future vaccine policy in a population with a high immunity against SARS‐CoV‐2 conferred by previous infections and/or vaccinations. Vaccine policy must consider the magnitude of the risks conferred by new infection(s) with current and evolving SARS‐CoV‐2 variants, how these risks vary in different groups of individuals, how to balance these risks against the apparently small, but existent, risks of harms of vaccination, and the cost–benefit of different options. More evidence from randomized controlled trials and continuously accumulating national health data is required to inform shared decision‐making with people who consider vaccination options. Vaccine policy makers should cautiously weight what vaccination schedules are needed, and refrain from urging frequent vaccine boosters unless supported by sufficient evidence. [ABSTRACT FROM AUTHOR]

Social media and new tools for engagement offer democratic platforms for enhancing constructive scientific criticism which had previously been limited. Constructive criticism can now be massive, timely and open. However, new options have also enhanced obsessive criticism. Obsessive criticism tends to focus on one or a handful of individuals and their work, often includes ad hominem aspects, and the critics often lack field‐specific skills and technical expertise. Typical behaviours include: repetitive and persistent comments (including sealioning), lengthy commentaries/tweetorials/responses often longer than the original work, strong degree of moralizing, distortion of the underlying work, argumentum ad populum, calls to suspend/censor/retract the work or the author, guilt‐by‐association, reputational tarnishing, large gains in followers specifically through attacks, finding and positing sensitive personal information, anonymity or pseudonymity, social media campaigning, and unusual ratio of criticism to pursuit of one's research agenda. These behaviours may last months or years. Prevention and treatment options may include awareness, identifying and working around aggravating factors, placing limits on the volume by editors, constructive pairing of commissioned editorials, incorporation of some hot debates from unregulated locations such as social media or PubPeer to the pages of scientific journals, preserving decency and focusing on evidence and arguments and avoiding personal statements, or (in some cases) ignoring. We need more research on the role of social media and obsessive criticism on an evolving cancel culture, the social media credibility, the use/misuse of anonymity and pseudonymity, and whether potential interventions from universities may improve or further weaponize scientific criticism. [ABSTRACT FROM AUTHOR]

Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired. [ABSTRACT FROM AUTHOR]

Massive scientific productivity accompanied the COVID-19 pandemic. We evaluated the citation impact of COVID-19 publications relative to all scientific work published in 2020 to 2021 and assessed the impact on scientist citation profiles. Using Scopus data until August 1, 2021, COVID-19 items accounted for 4% of papers published, 20% of citations received to papers published in 2020 to 2021, and >30% of citations received in 36 of the 174 disciplines of science (up to 79.3% in general and internal medicine). Across science, 98 of the 100 most-cited papers published in 2020 to 2021 were related to COVID-19; 110 scientists received ≥10,000 citations for COVID-19 work, but none received ≥10,000 citations for non–COVID-19 work published in 2020 to 2021. For many scientists, citations to their COVID-19 work already accounted for more than half of their total career citation count. Overall, these data show a strong covidization of research citations across science, with major impact on shaping the citation elite. [ABSTRACT FROM AUTHOR]

This study assesses data sharing status and lifting of embargoes in randomized clinical trials from top medical journals 3 to 5 years after publication. [ABSTRACT FROM AUTHOR]

With the establishment of large biobanks, discovery of single nucleotide variants (SNVs, also known as single nucleotide polymorphisms (SNVs)) associated with various phenotypes has accelerated. An open question is whether genome-wide significant SNVs identified in earlier genome-wide association studies (GWAS) are replicated in later GWAS conducted in biobanks. To address this, we examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, "discovery" GWAS and a later, "replication" GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNVs (of which 6289 reached P < 5e−8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0%; although lower for binary than quantitative phenotypes (58.1% versus 94.8% respectively). There was a 18.0% decrease in SNV effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNV effect size, phenotype trait (binary or quantitative), and discovery P value, we built and validated a model that predicted SNV replication with area under the Receiver Operator Curve = 0.90. While non-replication may reflect lack of power rather than genuine false-positives, these results provide insights about which discovered associations are likely to be replicated across subsequent GWAS. [ABSTRACT FROM AUTHOR]

Background: Estimates of community spread and infection fatality rate (IFR) of COVID-19 have varied across studies. Efforts to synthesize the evidence reach seemingly discrepant conclusions. Methods: Systematic evaluations of seroprevalence studies that had no restrictions based on country and which estimated either total number of people infected and/or aggregate IFRs were identified. Information was extracted and compared on eligibility criteria, searches, amount of evidence included, corrections/adjustments of seroprevalence and death counts, quantitative syntheses and handling of heterogeneity, main estimates and global representativeness. Results: Six systematic evaluations were eligible. Each combined data from 10 to 338 studies (9-50 countries), because of different eligibility criteria. Two evaluations had some overt flaws in data, violations of stated eligibility criteria and biased eligibility criteria (eg excluding studies with few deaths) that consistently inflated IFR estimates. Perusal of quantitative synthesis methods also exhibited several challenges and biases. Global representativeness was low with 78%-100% of the evidence coming from Europe or the Americas; the two most problematic evaluations considered only one study from other continents. Allowing for these caveats, four evaluations largely agreed in their main final estimates for global spread of the pandemic and the other two evaluations would also agree after correcting overt flaws and biases. Conclusions: All systematic evaluations of seroprevalence data converge that SARS-CoV- 2 infection is widely spread globally. Acknowledging residual uncertainties, the available evidence suggests average global IFR of ~0.15% and ~1.5-2.0 billion infections by February 2021 with substantial differences in IFR and in infection spread across continents, countries and locations. [ABSTRACT FROM AUTHOR]

As of October 2020, there are >1 million documented deaths with COVID‐19. Excess deaths can be caused by both COVID‐19 and the measures taken. COVID‐19 shows extremely strong risk stratification across age, socioeconomic factors, and clinical factors. Calculation of years‐of‐life‐lost from COVID‐19 is methodologically challenging and can yield misleading over‐estimates. Many early deaths may have been due to suboptimal management, malfunctional health systems, hydroxychloroquine, sending COVID‐19 patients to nursing homes, and nosocomial infections; such deaths are partially avoidable moving forward. About 10% of the global population may be infected by October 2020. Global infection fatality rate is 0.15‐0.20% (0.03‐0.04% in those <70 years), with large variability across locations with different age‐structure, institutionalization rates, socioeconomic inequalities, population‐level clinical risk profile, public health measures, and health care. There is debate on whether at least 60% of the global population must be infected for herd immunity, or, conversely, mixing heterogeneity and pre‐existing cross‐immunity may allow substantially lower thresholds. Simulations are presented with a total of 1.58‐8.76 million COVID‐19 deaths over 5‐years (1/2020‐12/2024) globally (0.5‐2.9% of total global deaths). The most favorable figures in that range would be feasible if high risk groups can be preferentially protected with lower infection rates than the remaining population. Death toll may also be further affected by potential availability of effective vaccines and treatments, optimal management and measures taken, COVID‐19 interplay with influenza and other health problems, reinfection potential, and any chronic COVID‐19 consequences. Targeted, precise management of the pandemic and avoiding past mistakes would help minimize mortality. [ABSTRACT FROM AUTHOR]

We aimed to assess whether Nobel prizes (widely considered the most prestigious award in science) are clustering in work done in a few specific disciplines. We mapped the key Nobel prize-related publication of each laureate awarded the Nobel Prize in Medicine, Physics, and Chemistry (1995–2017). These key papers mapped in only narrow sub-regions of a 91,726-cluster map of science created from 63 million Scopus-indexed published items. For each key Nobel paper, a median of 435 (range 0 to 88383) other Scopus-indexed items were published within one year and were more heavily cited than the Nobel paper. Of the 114 high-level domains that science can be divided into, only 36 have had a Nobel prize. Five of the 114 domains (particle physics [14%], cell biology [12.1%], atomic physics [10.9%], neuroscience [10.1%], molecular chemistry [5.3%]) have the lion's share, accounting in total for 52.4% of the Nobel prizes. Using a more granular classification with 849 sub-domains shows that only 71 of these sub-domains (8.3%) have at least one Nobel-related paper. Similar clustering was seen when we mapped all the 40,819 Scopus-indexed publications representing the career-long output of all the Nobel laureates. In conclusion, work resulting in Nobel prizes is concentrated in a small minority of scientific disciplines. [ABSTRACT FROM AUTHOR]

The evolving coronavirus disease 2019 (COVID-19) pandemic[1] is certainly cause for concern. However, if only part of resources mobilized to implement extreme measures for COVID-19 had been invested towards enhancing influenza vaccination uptake, tens of thousands of influenza deaths might have been averted. Case fatality of 8% has been described in outbreaks among nursing home elderly.[17] Leaving the well-known and highly lethal SARS and MERS coronaviruses aside, other coronaviruses probably have infected millions of people and have killed thousands. An outbreak of human coronavirus OC43 infection and serological cross-reactivity with SARS coronavirus. [Extracted from the article]

Background: Different analytical approaches can influence the associations estimated in observational studies. We assessed the variability of effect estimates reported within and across observational studies evaluating the impact of alcohol on breast cancer.Methods: We abstracted largest harmful, largest protective and smallest (closest to the null value of 1.0) relative risk estimates in studies included in a recent alcohol-breast cancer meta-analysis, and recorded how they differed based on five model specification characteristics, including exposure definition, exposure contrast levels, study populations, adjustment covariates and/or model approaches. For each study, we approximated vibration of effects by dividing the largest by the smallest effect estimate [i.e. ratio of odds ratio (ROR)].Results: Among 97 eligible studies, 85 (87.6%) reported both harmful and protective relative effect estimates for an alcohol-breast cancer relationship, which ranged from 1.1 to 17.9 and 0.0 to 1.0, respectively. The RORs comparing the largest and smallest estimates in value ranged from 1.0 to 106.2, with a median of 3.0 [interquartile range (IQR) 2.0-5.2]. One-third (35, 36.1%) of the RORs were based on extreme effect estimates with at least three different model specification characteristics; the vast majority (87, 89.7%) had different exposure definitions or contrast levels. Similar vibrations of effect were observed when only extreme estimates with differences based on study populations and/or adjustment covariates were compared.Conclusions: Most observational studies evaluating the impact of alcohol on breast cancer report relative effect estimates for the same associations that diverge by >2-fold. Therefore, observational studies should estimate the vibration of effects to provide insight regarding the stability of findings. [ABSTRACT FROM AUTHOR]

Certain limitations of evidence available on drugs and devices at the time of market approval often persist in the post-marketing period. Often, post-marketing research landscape is fragmented. When regulatory agencies require pharmaceutical and device manufacturers to conduct studies in the post-marketing period, these studies might remain incomplete many years after approval. Even when completed, many post-marketing studies lack meaningful active comparators, have observational designs, and might not collect patient-relevant outcomes. Regulators, in collaboration with the industry and patients, ought to ensure that the key questions unanswered at the time of drug and device approval are resolved in a timely fashion during the post-marketing phase. We propose a set of seven key guiding principles that we believe will provide the necessary incentives for pharmaceutical and device manufacturers to generate comparative data in the post-marketing period. First, regulators (for drugs and devices), notified bodies (for devices in Europe), health technology assessment organisations, and payers should develop customised evidence generation plans, ensuring that future post-approval studies address any limitations of the data available at the time of market entry impacting the benefit-risk profiles of drugs and devices. Second, post-marketing studies should be designed hierarchically: priority should be given to efforts aimed at evaluating a product's net clinical benefit in randomised trials compared with current known effective therapy, whenever possible, to address common decisional dilemmas. Third, post-marketing studies should incorporate active comparators as appropriate. Fourth, use of non-randomised studies for the evaluation of clinical benefit in the post-marketing period should be limited to instances when the magnitude of effect is deemed to be large or when it is possible to reasonably infer the comparative benefits or risks in settings, in which doing a randomised trial is not feasible. Fifth, efficiency of randomised trials should be improved by streamlining patient recruitment and data collection through innovative design elements. Sixth, governments should directly support and facilitate the production of comparative post-marketing data by investing in the development of collaborative research networks and data systems that reduce the complexity, cost, and waste of rigorous post-marketing research efforts. Last, financial incentives and penalties should be developed or more actively reinforced. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance is one of the most important threats to global health security. A range of Gram-negative bacteria associated with high morbidity and mortality are now resistant to almost all available antibiotics. In this context of urgency to develop novel drugs, new antibiotics for multidrug-resistant Gram-negative bacteria (namely, ceftazidime-avibactam, plazomicin, and meropenem-vaborbactam) have been approved by regulatory authorities based on non-inferiority trials that provided no direct evidence of their efficacy against multidrug-resistant bacteria such as Enterobacteriaceae spp, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, Burkholderia cepacia, and Acinetobacter baumannii. The use of non-inferiority and superiority trials, and selection of appropriate and optimal study designs, remains a major challenge in the development, registration, and post-marketing implementation of new antibiotics. Using an example of the development process of ceftazidime-avibactam, we propose a strategy for a new research framework based on adaptive randomised clinical trials. The operational research strategy has the aim of assessing the efficacy of new antibiotics in special groups of patients, such as those infected with multidrug-resistant bacteria, who were not included in earlier phase studies, and for whom it is important to establish an appropriate standard of care. [ABSTRACT FROM AUTHOR]

The objective of this meta‐epidemiological study was to explore the impact of attrition rates on treatment effect estimates in randomised trials of chronic inflammatory diseases (CID) treated with biological and targeted synthetic disease‐modifying drugs. We sampled trials from Cochrane reviews. Attrition rates and primary endpoint results were retrieved from trial publications; Odds ratios (ORs) were calculated from the odds of withdrawing in the experimental intervention compared to the control comparison groups (i.e., differential attrition), as well as the odds of achieving a clinical response (i.e., the trial outcome). Trials were combined using random effects restricted maximum likelihood meta‐regression models and associations between estimates of treatment effects and attrition rates were analysed. From 37 meta‐analyses, 179 trials were included, and 163 were analysed (301 randomised comparisons; n = 62,220 patients). Overall, the odds of withdrawal were lower in the experimental compared to control groups (random effects summary OR = 0.45, 95% CI, 0.41–0.50). The corresponding overall treatment effects were large (random effects summary OR = 4.43, 95% CI 3.92–4.99) with considerable heterogeneity across interventions and clinical specialties (I2 = 85.7%). The ORs estimating treatment effect showed larger treatment benefits when the differential attrition was more prominent with more attrition in the control group (OR = 0.73, 95% CI 0.55–0.96). Higher attrition rates from the control arm are associated with larger estimated benefits of treatments with biological or targeted synthetic disease‐modifying drugs in CID trials; differential attrition may affect estimates of treatment benefit in randomised trials. [ABSTRACT FROM AUTHOR]

The article announces the official call for abstracts for the Ninth International Congress on Peer Review and Scientific Publication to be held on September 8-10, 2022, citing the deadline for abstract submission on January 31, 2022.

This Viewpoint examines the increase in "mega-journals" (prolific publishers of medical articles) and both the opportunities and threats to scientific research they present. [ABSTRACT FROM AUTHOR]

The authors reflect on the quality, integrity, and utility of COVID-19 science, as well as the opportunities for public health researchers on the issue. Other topics include the rigorous standards of study design and conduct, the long-term effects of the COVID-19 pandemic, the side effects of the COVID lockdowns, and the validity of case and mortality national registries.

A causal association of air pollution with mental diseases is an intriguing possibility raised in a Short Report just published in PLOS Biology. Despite analyses involving large data sets, the available evidence has substantial shortcomings, and a long series of potential biases may invalidate the observed associations. Only bipolar disorder shows consistent results, with similar effects across United States and Denmark data sets, but the effect has modest magnitude, appropriate temporality is not fully secured, and biological gradient, plausibility, coherence, and analogy offer weak support. The signal seems to persist in some robustness analyses, but more analyses by multiple investigators, including contrarians, are necessary. Broader public sharing of data sets would also enhance transparency. [ABSTRACT FROM AUTHOR]

Citation metrics are widely used and misused. We have created a publicly available database of 100,000 top scientists that provides standardized information on citations, h-index, coauthorship-adjusted hm-index, citations to papers in different authorship positions, and a composite indicator. Separate data are shown for career-long and single-year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 176 subfields. Field- and subfield-specific percentiles are also provided for all scientists who have published at least five papers. Career-long data are updated to end of 2017 and to end of 2018 for comparison. [ABSTRACT FROM AUTHOR]

Family history is a strong risk factor for many common chronic diseases and summarizes shared environmental and genetic risk, but how this increased risk is mediated is unknown. We developed a "family history–wide association study" (FamWAS) to systematically and comprehensively test clinical and environmental quantitative traits (CEQTs) for their association with family history of disease. We implemented our method on 457 CEQTs for association with family history of diabetes, asthma, and coronary heart disease (CHD) in 42,940 adults spanning 8 waves of the 1999–2014 US National Health and Nutrition Examination Survey. We conducted pooled analyses of the 8 survey waves and analyzed trait associations using survey-weighted logistic regression. We identified 172 (37.6% of total), 32 (7.0%), and 78 (17.1%) CEQTs associated with family history of diabetes, asthma, and CHD, respectively, in subcohorts of individuals without the respective disease. Twenty associated CEQTs were shared across family history of diabetes, asthma, and CHD, far more than expected by chance. FamWAS can examine traits not previously studied in association with family history and uncover trait overlap, highlighting a putative shared mechanism by which family history influences disease risk. [ABSTRACT FROM AUTHOR]

Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of individuals, almost any desired relationship can be inferred with a suitable combination of covariates or analytic choices. Inspired by the genome-wide association study analysis paradigm that has transformed human genetics, X-wide association studies or "XWAS" have emerged as a popular approach to systematically analyzing nongenetic data sets and guarding against false positives. However, these studies often yield hundreds or thousands of associations characterized by modest effect sizes and miniscule P values. Many of these associations will be spurious and emerge due to confounding and other biases. One way of characterizing confounding in the genomics paradigm is the genomic inflation factor. An analogous "X-wide inflation factor," denoted λ X , can be defined and applied to published XWAS. Effects that arise in XWAS may be prioritized using replication, triangulation, quantification of measurement error, contextualization of each effect in the distribution of all effect sizes within a field, and pre-registration. Criteria like those of Bradford Hill need to be reconsidered in light of exposure-wide epidemiology to prioritize signals among signals. [ABSTRACT FROM AUTHOR]

The article focuses on concerns raised in response to a recent Viewpoint discussing the threats and opportunities associated with the rapid growth of mega-journals. It authors express concerns about issues not discussed in the original Viewpoint, such as the integrity of medical literature due to special issue manipulation, retraction rates, and paper-mill articles in mega-journals.

Space medicine is a vital discipline with often time-intensive and costly projects and constrained opportunities for studying various elements such as space missions, astronauts, and simulated environments. Moreover, private interests gain increasing influence in this discipline. In scientific disciplines with these features, transparent and rigorous methods are essential. Here, we undertook an evaluation of transparency indicators in publications within the field of space medicine. A meta-epidemiological assessment of PubMed Central Open Access (PMC OA) eligible articles within the field of space medicine was performed for prevalence of code sharing, data sharing, pre-registration, conflicts of interest, and funding. Text mining was performed with the rtransparent text mining algorithms with manual validation of 200 random articles to obtain corrected estimates. Across 1215 included articles, 39 (3%) shared code, 258 (21%) shared data, 10 (1%) were registered, 110 (90%) contained a conflict-of-interest statement, and 1141 (93%) included a funding statement. After manual validation, the corrected estimates for code sharing, data sharing, and registration were 5%, 27%, and 1%, respectively. Data sharing was 32% when limited to original articles and highest in space/parabolic flights (46%). Overall, across space medicine we observed modest rates of data sharing, rare sharing of code and almost non-existent protocol registration. Enhancing transparency in space medicine research is imperative for safeguarding its scientific rigor and reproducibility. [ABSTRACT FROM AUTHOR]

Many sources document problems that jeopardize the trustworthiness of systematic reviews. This is a major concern given their potential to influence patient care and impact people's lives. Responsibility for producing trustworthy conclusions on the evidence in systematic reviews is borne primarily by authors who need the necessary training and resources to correctly report on the current knowledge base. Peer reviewers and editors are also accountable; they must ensure that systematic reviews are accurate by demonstrating proper methods. To support all these stakeholders, we attempt to distill the sprawling guidance that is currently available in our recent co‐publication about best tools and practices for systematic reviews. We specifically address how to meet methodological conduct standards applicable to key components of systematic reviews. In this complementary invited review, we place these standards in the context of good scholarship principles for systematic review development. Our intention is to reach a broad audience and potentially improve the trustworthiness of evidence syntheses published in the developmental medicine literature and beyond. [ABSTRACT FROM AUTHOR]

STUDY QUESTION What were the frequency and temporal trends of reporting P -values and effect measures in the abstracts of reproductive medicine studies in 1990–2022, how were reported P -values distributed, and what proportion of articles that present with statistical inference reported statistically significant results, i.e. 'positive' results? SUMMARY ANSWER Around one in six abstracts reported P -values alone without effect measures, while the prevalence of effect measures, whether reported alone or accompanied by P -values, has been increasing, especially in meta-analyses and randomized controlled trials (RCTs); the reported P -values were frequently observed around certain cut-off values, notably at 0.001, 0.01, or 0.05, and among abstracts present with statistical inference (i.e. P -value, CIs, or significant terms), a large majority (77%) reported at least one statistically significant finding. WHAT IS KNOWN ALREADY Publishing or reporting only results that show a 'positive' finding causes bias in evaluating interventions and risk factors and may incur adverse health outcomes for patients.   Despite efforts to minimize publication reporting bias in medical research, it remains unclear whether the magnitude and patterns of the bias have changed over time. STUDY DESIGN, SIZE, DURATION We studied abstracts of reproductive medicine studies from 1990 to 2022. The reproductive medicine studies were published in 23 first-quartile journals under the category of Obstetrics and Gynaecology and Reproductive Biology in Journal Citation Reports and 5 high-impact general medical journals (The Journal of the American Medical Association , The Lancet , The BMJ , The New England Journal of Medicine , and PLoS Medicine). Articles without abstracts, animal studies, and non-research articles, such as case reports or guidelines, were excluded. PARTICIPANTS/MATERIALS, SETTING, METHODS Automated text-mining was used to extract three types of statistical significance reporting, including P -values, CIs, and text description. Meanwhile, abstracts were text-mined for the presence of effect size metrics and Bayes factors. Five hundred abstracts were randomly selected and manually checked for the accuracy of automatic text extraction. The extracted statistical significance information was then analysed for temporal trends and distribution in general as well as in subgroups of study designs and journals. MAIN RESULTS AND THE ROLE OF CHANCE A total of 24 907 eligible reproductive medicine articles were identified from 170 739 screened articles published in 28 journals. The proportion of abstracts not reporting any statistical significance inference halved from 81% (95% CI, 76–84%) in 1990 to 40% (95% CI, 38–44%) in 2021, while reporting P -values alone remained relatively stable, at 15% (95% CI, 12–18%) in 1990 and 19% (95% CI, 16–22%) in 2021. By contrast, the proportion of abstracts reporting effect measures alone increased considerably from 4.1% (95% CI, 2.6–6.3%) in 1990 to 26% (95% CI, 23–29%) in 2021. Similarly, the proportion of abstracts reporting effect measures together with P -values showed substantial growth from 0.8% (95% CI, 0.3–2.2%) to 14% (95% CI, 12–17%) during the same timeframe. Of 30 182 statistical significance inferences, 56% (n = 17 077) conveyed statistical inferences via P -values alone, 30% (n = 8945) via text description alone such as significant or non-significant, 9.3% (n = 2820) via CIs alone, and 4.7% (n = 1340) via both CI and P -values. The reported P -values (n = 18 417), including both a continuum of P -values and dichotomized P -values, were frequently observed around common cut-off values such as 0.001 (20%), 0.05 (16%), and 0.01 (10%). Of the 13 200 reproductive medicine abstracts containing at least one statistical inference, 77% of abstracts made at least one statistically significant statement. Among articles that reported statistical inference, a decline in the proportion of making at least one statistically significant inference was only seen in RCTs, dropping from 71% (95% CI, 48–88%) in 1990 to 59% (95% CI, 42–73%) in 2021, whereas the proportion in the rest of study types remained almost constant over the years. Of abstracts that reported P -value, 87% (95% CI, 86–88%) reported at least one statistically significant P -value; it was 92% (95% CI, 82–97%) in 1990 and reached its peak at 97% (95% CI, 93–99%) in 2001 before declining to 81% (95% CI, 76–85%) in 2021. LIMITATIONS, REASONS FOR CAUTION First, our analysis focused solely on reporting patterns in abstracts but not full-text papers; however, in principle, abstracts should include condensed impartial information and avoid selective reporting. Second, while we attempted to identify all types of statistical significance reporting, our text mining was not flawless. However, the manual assessment showed that inaccuracies were not frequent. WIDER IMPLICATIONS OF THE FINDINGS There is a welcome trend that effect measures are increasingly reported in the abstracts of reproductive medicine studies, specifically in RCTs and meta-analyses. Publication reporting bias remains a major concern. Inflated estimates of interventions and risk factors could harm decisions built upon biased evidence, including clinical recommendations and planning of future research. STUDY FUNDING/COMPETING INTEREST(S) No funding was received for this study. B.W.M. is supported by an NHMRC Investigator grant (GNT1176437); B.W.M. reports research grants and travel support from Merck and consultancy from Merch and ObsEva. W.L. is supported by an NHMRC Investigator Grant (GNT2016729). Q.F. reports receiving a PhD scholarship from Merck. The other author has no conflict of interest to declare. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Background: Selective reporting of results from only well-performing cut-offs leads to biased estimates of accuracy in primary studies of questionnaire-based screening tools and in meta-analyses that synthesize results. Individual participant data meta-analysis (IPDMA) of sensitivity and specificity at each cut-off via bivariate random-effects models (BREMs) can overcome this problem. However, IPDMA is laborious and depends on the ability to successfully obtain primary datasets, and BREMs ignore the correlation between cut-offs within primary studies. Methods: We compared the performance of three recent multiple cut-off models developed by Steinhauser et al., Jones et al., and Hoyer and Kuss, that account for missing cut-offs when meta-analyzing diagnostic accuracy studies with multiple cut-offs, to BREMs fitted at each cut-off. We used data from 22 studies of the accuracy of the Edinburgh Postnatal Depression Scale (EPDS; 4475 participants, 758 major depression cases). We fitted each of the three multiple cut-off models and BREMs to a dataset with results from only published cut-offs from each study (published data) and an IPD dataset with results for all cut-offs (full IPD data). We estimated pooled sensitivity and specificity with 95% confidence intervals (CIs) for each cut-off and the area under the curve. Results: Compared to the BREMs fitted to the full IPD data, the Steinhauser et al., Jones et al., and Hoyer and Kuss models fitted to the published data produced similar receiver operating characteristic curves; though, the Hoyer and Kuss model had lower area under the curve, mainly due to estimating slightly lower sensitivity at lower cut-offs. When fitting the three multiple cut-off models to the full IPD data, a similar pattern of results was observed. Importantly, all models had similar 95% CIs for sensitivity and specificity, and the CI width increased with cut-off levels for sensitivity and decreased with an increasing cut-off for specificity, even the BREMs which treat each cut-off separately. Conclusions: Multiple cut-off models appear to be the favorable methods when only published data are available. While collecting IPD is expensive and time consuming, IPD can facilitate subgroup analyses that cannot be conducted with published data only. [ABSTRACT FROM AUTHOR]

This Formal Comment presents an update to citation databases of top-cited scientists across all scientific fields, including more granular information on diverse indicators. [ABSTRACT FROM AUTHOR]

The authors offer information on the Ninth International Congress on Peer Review and Scientific Publication which will be held in Chicago, Illinois on September 12-14, 2019. Topics discussed include the goal of the event to encourage research on the quality and credibility of peer review and scientific publication, principles and practices of the Congress and the initial start on biomedicine and expanded to cover other issues including social and physical sciences.

The vast majority of scientific articles published to-date have not been accompanied by concomitant publication of the underlying research data upon which they are based. This state of affairs precludes the routine re-use and re-analysis of research data, undermining the efficiency of the scientific enterprise, and compromising the credibility of claims that cannot be independently verified. It may be especially important to make data available for the most influential studies that have provided a foundation for subsequent research and theory development. Therefore, we launched an initiative—the Data Ark—to examine whether we could retrospectively enhance the preservation and accessibility of important scientific data. Here we report the outcome of our efforts to retrieve, preserve, and liberate data from 111 of the most highly-cited articles published in psychology and psychiatry between 2006–2011 (n = 48) and 2014–2016 (n = 63). Most data sets were not made available (76/111, 68%, 95% CI [60, 77]), some were only made available with restrictions (20/111, 18%, 95% CI [10, 27]), and few were made available in a completely unrestricted form (15/111, 14%, 95% CI [5, 22]). Where extant data sharing systems were in place, they usually (17/22, 77%, 95% CI [54, 91]) did not allow unrestricted access. Authors reported several barriers to data sharing, including issues related to data ownership and ethical concerns. The Data Ark initiative could help preserve and liberate important scientific data, surface barriers to data sharing, and advance community discussions on data stewardship. [ABSTRACT FROM AUTHOR]

P values represent a widely used, but pervasively misunderstood and fiercely contested method of scientific inference. Display items, such as figures and tables, often containing the main results, are an important source of P values. We conducted a survey comparing the overall use of P values and the occurrence of significant P values in display items of a sample of articles in the three top multidisciplinary journals (Nature, Science, PNAS) in 2017 and, respectively, in 1997. We also examined the reporting of multiplicity corrections and its potential influence on the proportion of statistically significant P values. Our findings demonstrated substantial and growing reliance on P values in display items, with increases of 2.5 to 14.5 times in 2017 compared to 1997. The overwhelming majority of P values (94%, 95% confidence interval [CI] 92% to 96%) were statistically significant. Methods to adjust for multiplicity were almost non-existent in 1997, but reported in many articles relying on P values in 2017 (Nature 68%, Science 48%, PNAS 38%). In their absence, almost all reported P values were statistically significant (98%, 95% CI 96% to 99%). Conversely, when any multiplicity corrections were described, 88% (95% CI 82% to 93%) of reported P values were statistically significant. Use of Bayesian methods was scant (2.5%) and rarely (0.7%) articles relied exclusively on Bayesian statistics. Overall, wider appreciation of the need for multiplicity corrections is a welcome evolution, but the rapid growth of reliance on P values and implausibly high rates of reported statistical significance are worrisome. [ABSTRACT FROM AUTHOR]

The article focuses on improving the integrity of published science and also offers information on taxonomy of retractions and corrections.

Plantar fascial disorder is comprised of plantar fasciitis and plantar fibromatosis. Plantar fasciitis is the most common cause of heel pain, especially for athletes involved in running and jumping sports. Plantar fibromatosis is a rare fibrous hyperproliferation of the deep connective tissue of the foot. To identify genetic loci associated with plantar fascial disorders, a genome-wide association screen was performed using publically available data from the Research Program in Genes, Environment and Health including 21,624 cases of plantar fascial disorders and 80,879 controls. One indel (chr5:118704153:D) and one SNP (rs62051384) showed an association with plantar fascial disorders at genome-wide significance (p < 5 x 10-8) with small effects (odds ratios = 0.93 and 1.07 per allele, respectively). The indel chr5:118704153:D is located within TNFAIP8 (encodes a protein induced by TNF alpha) and rs62051384 is located within WWP2 (which is involved in proteasomal degradation). These DNA variants may be informative in explaining why some individuals are at higher risk for plantar fascial disorders than others. [ABSTRACT FROM AUTHOR]

Meta-research is the study of research itself: its methods, reporting, reproducibility, evaluation, and incentives. Given that science is the key driver of human progress, improving the efficiency of scientific investigation and yielding more credible and more useful research results can translate to major benefits. The research enterprise grows very fast. Both new opportunities for knowledge and innovation and new threats to validity and scientific integrity emerge. Old biases abound, and new ones continuously appear as novel disciplines emerge with different standards and challenges. Meta-research uses an interdisciplinary approach to study, promote, and defend robust science. Major disruptions are likely to happen in the way we pursue scientific investigation, and it is important to ensure that these disruptions are evidence based. [ABSTRACT FROM AUTHOR]

Most physicians and other healthcare professionals are unaware of the pervasiveness of poor quality clinical evidence that contributes considerably to overuse, underuse, avoidable adverse events, missed opportunities for right care and wasted healthcare resources. The Medical Misinformation Mess comprises four key problems. First, much published medical research is not reliable or is of uncertain reliability, offers no benefit to patients, or is not useful to decision makers. Second, most healthcare professionals are not aware of this problem. Third, they also lack the skills necessary to evaluate the reliability and usefulness of medical evidence. Finally, patients and families frequently lack relevant, accurate medical evidence and skilled guidance at the time of medical decision-making. Increasing the reliability of available, published evidence may not be an imminently reachable goal. Therefore, efforts should focus on making healthcare professionals, more sensitive to the limitations of the evidence, training them to do critical appraisal, and enhancing their communication skills so that they can effectively summarize and discuss medical evidence with patients to improve decision-making. Similar efforts may need to target also patients, journalists, policy makers, the lay public and other healthcare stakeholders. [ABSTRACT FROM AUTHOR]

Data continue to accumulate indicating that many systematic reviews are methodologically flawed, biased, redundant, or uninformative. Some improvements have occurred in recent years based on empirical methods research and standardization of appraisal tools; however, many authors do not routinely or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may automatically accept evidence syntheses (and clinical practice guidelines based on their conclusions) as trustworthy. A plethora of methods and tools are recommended for the development and evaluation of evidence syntheses. It is important to understand what these are intended to do (and cannot do) and how they can be utilized. Our objective is to distill this sprawling information into a format that is understandable and readily accessible to authors, peer reviewers, and editors. In doing so, we aim to promote appreciation and understanding of the demanding science of evidence synthesis among stakeholders. We focus on well‐documented deficiencies in key components of evidence syntheses to elucidate the rationale for current standards. The constructs underlying the tools developed to assess reporting, risk of bias, and methodological quality of evidence syntheses are distinguished from those involved in determining overall certainty of a body of evidence. Another important distinction is made between those tools used by authors to develop their syntheses as opposed to those used to ultimately judge their work. Exemplar methods and research practices are described, complemented by novel pragmatic strategies to improve evidence syntheses. The latter include preferred terminology and a scheme to characterize types of research evidence. We organize best practice resources in a Concise Guide that can be widely adopted and adapted for routine implementation by authors and journals. Appropriate, informed use of these is encouraged, but we caution against their superficial application and emphasize their endorsement does not substitute for in‐depth methodological training. By highlighting best practices with their rationale, we hope this guidance will inspire further evolution of methods and tools that can advance the field. [ABSTRACT FROM AUTHOR]

Finally, medical educators include both people who define themselves as educators (e.g., "professor" or "residency director") and others who may be even more influential educators, yet they don't even know it (e.g., patients). Medical education combines science and art. [Extracted from the article]