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Background: Scientific publications have been growing exponentially, contributing to an oversaturated information environment. Quantifying a research output's impact and reach cannot be solely measured by traditional metrics like citation counts as these have a lag time and are largely focused on an academic audience. There is increasing recognition to consider 'alternative metrics' or altmetrics to measure more immediate and broader impacts of research. Better understanding of altmetrics can help researchers better navigate evolving information environments and changing appetites for different types of research., Objectives: Our study aims to: 1) analyse the amount and medium of Altmetric coverage of health research produced by Irish organisations (2017 - 2023), identifying changes over time and 2) investigate differences in the amount of coverage between clinical areas (e.g., nutrition vs. neurology)., Methods: Using Altmetric institutional access, we will gather data on research outputs published 1 January 2017 through 31 December 2023 from active Irish organisations with Research Organisation Registry (ROR) IDs. Outputs will be deduplicated and stratified by their Australian and New Zealand Standard Research Classification relating to ≥1 field of health research: Biological Sciences, Biomedical and Clinical Sciences, Chemical Sciences, Health Sciences, and Psychology. We will clean data using R and perform descriptive analyses, establishing counts and frequencies of coverage by clinical area and medium (e.g., traditional news, X, etc.); data will be plotted on a yearly and quarterly basis where appropriate., Results and Conclusions: Improved understanding of one's information environment can help researchers better navigate their local landscapes and identify pathways for more effective communication to the public. All R code will be made available open-source, allowing researchers to adapt it to evaluate their local landscapes., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Sharp MK et al.)

Objectives: To demonstrate how researchers can identify and translate reporting gaps from a systematic review into checklist items for reporting guidelines., Study Design and Setting: Good quality research reporting ensures transparency, reproducibility, and utility, facilitated by reporting guidelines. Conducting a systematic review is an essential step in the development of these guidelines. The Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network's toolkit (2010) assists researchers in this process and is due for an update to address current gaps and evolving research methods. One significant gap is the translation of systematic review findings into checklist items. Reflecting on our experience developing the ACcurate Consensus Reporting Document, we illustrate this translation process aiming to empower researchers developing reporting guidelines to address potential biases and promote transparency. We highlight the challenges faced and how they were addressed., Results: The systematic review search process was iterative, involving multiple adjustments to balance precision and sensitivity. Excessively stringent exclusion criteria may lead to missed valuable insights, especially when studies offer relevant content. An information specialist was invaluable in developing the search strategy. Key lessons learned include the necessity of maintaining flexibility and openness during data extraction, continuous adaptation based on panelist feedback, and promoting clear communication through understandable language. These principles can guide the development of future reporting guidelines and the updating of the EQUATOR toolkit, promoting transparency and robustness in research reporting., Conclusion: Maintaining flexibility, capturing evolving insights, clear communication, and accommodating changes in research and technologies are key to translating systematic review findings into effective reporting checklists., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: When research evidence is limited, inconsistent, or absent, healthcare decisions and policies need to be based on consensus amongst interested stakeholders. In these processes, the knowledge, experience, and expertise of health professionals, researchers, policymakers, and the public are systematically collected and synthesised to reach agreed clinical recommendations and/or priorities. However, despite the influence of consensus exercises, the methods used to achieve agreement are often poorly reported. The ACCORD (ACcurate COnsensus Reporting Document) guideline was developed to help report any consensus methods used in biomedical research, regardless of the health field, techniques used, or application. This explanatory document facilitates the use of the ACCORD checklist., Methods and Findings: This paper was built collaboratively based on classic and contemporary literature on consensus methods and publications reporting their use. For each ACCORD checklist item, this explanation and elaboration document unpacks the pieces of information that should be reported and provides a rationale on why it is essential to describe them in detail. Furthermore, this document offers a glossary of terms used in consensus exercises to clarify the meaning of common terms used across consensus methods, to promote uniformity, and to support understanding for consumers who read consensus statements, position statements, or clinical practice guidelines. The items are followed by examples of reporting items from the ACCORD guideline, in text, tables and figures., Conclusions: The ACCORD materials - including the reporting guideline and this explanation and elaboration document - can be used by anyone reporting a consensus exercise used in the context of health research. As a reporting guideline, ACCORD helps researchers to be transparent about the materials, resources (both human and financial), and procedures used in their investigations so readers can judge the trustworthiness and applicability of their results/recommendations., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: PL is a member of the UK EQUATOR Centre, based in the University of Oxford; EQUATOR promotes the use of reporting guidelines, many of which are developed using consensus methods, and she is personally involved in the development of other reporting guidelines. CCW is an employee, Director, and shareholder of Oxford PharmaGenesis Ltd., a Director of Oxford Health Policy Forum CIC, a Trustee of the Friends of the National Library of Medicine, and an Associate Fellow of Green Templeton College, University of Oxford. DT is co–editor-in-chief of the Journal of Clinical Epidemiology and chairs the Scientific Advisory Committee for the Centre for Biomedical Transparency. WG is an employee of Bristol Myers Squibb. NH is an employee of OPEN Health Communications. KG is an employee of AbbVie. KW and AC are employees of Oxford PharmaGenesis Ltd. AP, PB and EJvZ report no conflicts of interest. At the outset of the work, WG was an employee of Ipsen and NH was an employee of Ogilvy Health UK., (Copyright: © 2024 Logullo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: support from the funding bodies listed above for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. GSC is a National Institute for Health and Care Research (NIHR) senior investigator, the director of the UK EQUATOR Centre, editor-in-chief of BMC Diagnostic and Prognostic Research, and a statistics editor for The BMJ. KGMM is director of Health Innovation Netherlands and editor-in-chief of BMC Diagnostic and Prognostic Research. RDR is an NIHR senior investigator, a statistics editor for The BMJ, and receives royalties from textbooks Prognosis Research in Healthcare and Individual Participant Data Meta-Analysis. AKD is an NIHR senior investigator. EWL is the head of research at The BMJ. BG is a part time employee of HeartFlow and Kheiron Medical Technologies and holds stock options with both as part of the standard compensation package. SR receives royalties from Springer for the textbooks Targeted Learning: Causal Inference for Observational and Experimental Data and Targeted Learning: Causal Inference for Complex Longitudinal Studies. JCC receives honorariums as a current lay member on the UK NICE covid-19 expert panel and a citizen partner on the COVID-END Covid-19 Evidence Network to support decision making; was a lay member on the UK NIHR AI AWARD panel in 2020-22 and is a current lay member on the UK NHS England AAC Accelerated Access Collaborative NHS AI Laboratory Evaluation Advisory Group; is a patient fellow of the European Patients’ Academy on Therapeutic Innovation and a EURORDIS rare disease alumni; reports grants from the UK National Institute for Health and Care Research, European Commission, UK Cell Gene Catapult, University College London, and University of East Anglia; reports patient speaker fees from MEDABLE, Reuters Pharma events, Patients as Partners Europe, and EIT Health Scandinavia; reports consultancy fees from Roche Global, GlaxoSmithKline, the FutureScience Group and Springer Healthcare (scientific publishing), outside of the scope of the present work; and is a strategic board member of the UK Medical Research Council IASB Advanced Pain Discovery Platform initiative, Plymouth Institute of Health, and EU project Digipredict Edge AI-deployed Digital Twins for covid-19 Cardiovascular Disease. ALB is a paid consultant for Generate Biomedicines, Flagship Pioneering, Porter Health, FL97, Tessera, FL85; has an equity stake in Generate Biomedicines; and receives research funding support from GlaxoSmithKline, National Heart, Lung, and Blood Institute, and National Institute of Diabetes and Digestive and Kidney Diseases. No other conflicts of interests with this specific work are declared.

Background: In biomedical research, it is often desirable to seek consensus among individuals who have differing perspectives and experience. This is important when evidence is emerging, inconsistent, limited, or absent. Even when research evidence is abundant, clinical recommendations, policy decisions, and priority-setting may still require agreement from multiple, sometimes ideologically opposed parties. Despite their prominence and influence on key decisions, consensus methods are often poorly reported. Our aim was to develop the first reporting guideline dedicated to and applicable to all consensus methods used in biomedical research regardless of the objective of the consensus process, called ACCORD (ACcurate COnsensus Reporting Document)., Methods and Findings: We followed methodology recommended by the EQUATOR Network for the development of reporting guidelines: a systematic review was followed by a Delphi process and meetings to finalize the ACCORD checklist. The preliminary checklist was drawn from the systematic review of existing literature on the quality of reporting of consensus methods and suggestions from the Steering Committee. A Delphi panel (n = 72) was recruited with representation from 6 continents and a broad range of experience, including clinical, research, policy, and patient perspectives. The 3 rounds of the Delphi process were completed by 58, 54, and 51 panelists. The preliminary checklist of 56 items was refined to a final checklist of 35 items relating to the article title (n = 1), introduction (n = 3), methods (n = 21), results (n = 5), discussion (n = 2), and other information (n = 3)., Conclusions: The ACCORD checklist is the first reporting guideline applicable to all consensus-based studies. It will support authors in writing accurate, detailed manuscripts, thereby improving the completeness and transparency of reporting and providing readers with clarity regarding the methods used to reach agreement. Furthermore, the checklist will make the rigor of the consensus methods used to guide the recommendations clear for readers. Reporting consensus studies with greater clarity and transparency may enhance trust in the recommendations made by consensus panels., Competing Interests: PL is a member of the UK EQUATOR Centre, based in the University of Oxford; EQUATOR promotes the use of reporting guidelines, many of which are developed using consensus methods, and she is personally involved in the development of other reporting guidelines. WTG is an employee of Bristol Myers Squibb. KG is an employee and shareholder of AbbVie. APH, in the past 5 years, has worked with Reckitt Benckiser for the development of the definitions and management of gastro-oesophageal reflux disease. CCW is an employee, Director, and shareholder of Oxford PharmaGenesis Ltd, a Director of Oxford Health Policy Forum CIC, a Trustee of the Friends of the National Library of Medicine, and an Associate Fellow of Green Templeton College, University of Oxford. NH is an employee of OPEN Health Communications. ELH is an employee of Camino Communications. DT is co–editor-in-chief of the Journal of Clinical Epidemiology and chairs the Scientific Advisory Committee for the Centre for Biomedical Transparency. AP, PB and EJvZ report no conflicts of interest. At the outset of the work, NH was an employee of Ogilvy Health UK, WTG was an employee of Ipsen, and ELH was an employee of OPEN Health Communications at the time of manuscript development., (Copyright: © 2024 Gattrell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology., Study Design and Setting: We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices., Results: We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs., Conclusion: The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology., Competing Interests: Declaration of competing interest All authors declare no conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background and Objectives: We investigated the developing methods of reporting guidelines in the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network's database., Methods: In October 2018, we screened all records and excluded those not describing reporting guidelines from further investigation. Twelve researchers performed duplicate data extraction on bibliometrics, scope, development methods, presentation, and dissemination of all publications. Descriptive statistics were used to summarize the findings., Results: Of the 405 screened records, 262 described a reporting guidelines development. The number of reporting guidelines increased over the past 3 decades, from 5 in the 1990s and 63 in the 2000s to 157 in the 2010s. Development groups included 2-151 people. Literature appraisal was performed during the development of 56% of the reporting guidelines; 33% used surveys to gather external opinion on items to report; and 42% piloted or sought external feedback on their recommendations. Examples of good reporting for all reporting items were presented in 30% of the reporting guidelines. Eighteen percent of the reviewed publications included some level of spin., Conclusion: Reporting guidelines have been developed with varying methodology. Reporting guideline developers should use existing guidance and take an evidence-based approach, rather than base their recommendations on expert opinion of limited groups of individuals., Competing Interests: Declaration of Competing Interest No author has any financial interests to declare. All authors are members of, or have collaborations with, The EQUATOR Network., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: This study aimed to describe the methodologies used to develop and evaluate models that use artificial intelligence (AI) to analyse lung images in order to detect, segment (outline borders of), or classify pulmonary nodules as benign or malignant., Methods: In October 2019, we systematically searched the literature for original studies published between 2018 and 2019 that described prediction models using AI to evaluate human pulmonary nodules on diagnostic chest images. Two evaluators independently extracted information from studies, such as study aims, sample size, AI type, patient characteristics, and performance. We summarised data descriptively., Results: The review included 153 studies: 136 (89%) development-only studies, 12 (8%) development and validation, and 5 (3%) validation-only. CT scans were the most common type of image type used (83%), often acquired from public databases (58%). Eight studies (5%) compared model outputs with biopsy results. 41 studies (26.8%) reported patient characteristics. The models were based on different units of analysis, such as patients, images, nodules, or image slices or patches., Conclusion: The methods used to develop and evaluate prediction models using AI to detect, segment, or classify pulmonary nodules in medical imaging vary, are poorly reported, and therefore difficult to evaluate. Transparent and complete reporting of methods, results and code would fill the gaps in information we observed in the study publications., Advances in Knowledge: We reviewed the methodology of AI models detecting nodules on lung images and found that the models were poorly reported and had no description of patient characteristics, with just a few comparing models' outputs with biopsies results. When lung biopsy is not available, lung-RADS could help standardise the comparisons between the human radiologist and the machine. The field of radiology should not give up principles from the diagnostic accuracy studies, such as the choice for the correct ground truth, just because AI is used. Clear and complete reporting of the reference standard used would help radiologists trust in the performance that AI models claim to have. This review presents clear recommendations about the essential methodological aspects of diagnostic models that should be incorporated in studies using AI to help detect or segmentate lung nodules. The manuscript also reinforces the need for more complete and transparent reporting, which can be helped using the recommended reporting guidelines., (© 2023 The Authors. Published by the British Institute of Radiology.)

Pregnancy in chronic kidney disease (CKD) women is relatively rare, and the less risky choice of hemodialysis is unknown. The objective of this systematic review was to identify, systematically evaluate and summarize the available evidence on the efficacy and safety of hemodialysis strategies for pregnant CKD women. Sensitive search strategies were applied to six databases without data or language restrictions. Comparative (randomized and non-randomized) studies were prioritized. Two reviewers independently selected, extracted, and critically evaluated data from studies. The risk of bias assessment was performed using the ROBINS-I tool, considering the study design (non-randomized comparative observational studies). The certainty of the evidence was assessed using the GRADE approach. From 7210 references identified, six retrospective cohort studies were included (576 women). The effects of intensive hemodialysis (over 20 h/week) are uncertain for maternal and neonatal mortality (Peto odds ratio [OR] 0.85; 95% confidence interval [95% CI] 0.26-2.80), miscarriage (Peto OR 0, 38; 95% CI 0.12-1.23), stillbirths (Peto OR 0, 56; 95% CI 0.13-2.31), preterm birth (Peto OR 0.87; 95% CI 0.33-2.28), low birth weight (Peto OR 0.71; 95% CI 0.20-2.50) and congenital anomalies rates. The certainty of the evidence was very low due to studies methodological limitations and effect estimates imprecision. The uncertainty about intensive versus conventional hemodialysis effects for pregnant women with CKD and the imprecision in the estimated effects precludes any recommendation. The strategy choice must consider treatment availability, costs, and maternal social aspects until future studies provide more reliable evidence. PROSPERO CRD42021259237., (© 2022 Wiley Periodicals LLC.)

Objective: To identify evidence on the reporting quality of consensus methodology and to select potential checklist items for the ACcurate COnsensus Reporting Document (ACCORD) project to develop a consensus reporting guideline., Design: Systematic review., Data Sources: Embase, MEDLINE, Web of Science, PubMed, Cochrane Library, Emcare, Academic Search Premier and PsycINFO from inception until 7 January 2022., Eligibility Criteria: Studies, reviews and published guidance addressing the reporting quality of consensus methodology for improvement of health outcomes in biomedicine or clinical practice. Reports of studies using or describing consensus methods but not commenting on their reporting quality were excluded. No language restrictions were applied., Data Extraction and Synthesis: Screening and data extraction of eligible studies were carried out independently by two authors. Reporting quality items addressed by the studies were synthesised narratively., Results: Eighteen studies were included: five systematic reviews, four narrative reviews, three research papers, three conference abstracts, two research guidance papers and one protocol. The majority of studies indicated that the quality of reporting of consensus methodology could be improved. Commonly addressed items were: consensus panel composition; definition of consensus and the threshold for achieving consensus. Items least addressed were: public patient involvement (PPI); the role of the steering committee, chair, cochair; conflict of interest of panellists and funding. Data extracted from included studies revealed additional items that were not captured in the data extraction form such as justification of deviation from the protocol or incentives to encourage panellist response., Conclusion: The results of this systematic review confirmed the need for a reporting checklist for consensus methodology and provided a range of potential checklist items to report. The next step in the ACCORD project builds on this systematic review and focuses on reaching consensus on these items to develop the reporting guideline., Protocol Registration: https://osf.io/2rzm9., Competing Interests: Competing interests: PL is a member of the UK EQUATOR Centre, an organisation that promotes the use of reporting guidelines, many of which are developed using consensus methods, and she is personally involved in the development of other reporting guidelines. ELH has worked with Ogilvy Health UK on consensus projects. WTG is a former employee of Ipsen and is now employed by Bristol Myers Squib. AP is an editor at the BMJ and is a senior research scientist at Stanford University, where she is responsible for advising and seeking funding on Delphi and other research studies. EJvZ and ZF have no conflict of interest., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

The aim of this study was to identify, systematically assess and summarise the available evidence about the efficacy and safety of intravenous home antibiotic therapy. In this systematic review, we considered studies of adults with any kind of infection and recommended intravenous antibiotic therapy. We included studies comparing therapy given at the patient's home versus any other setting (other levels of healthcare services or sites). We performed wide and sensitive literature searches with strategies adjusted for each of the electronic databases, including CINAHL, ClinicalTrials.gov, Cochrane Library, Embase, Epistemonikos, Health System Evidence, LILACS, MEDLINE and grey literature (OpenGrey). We used the Cochrane risk-of-bias and GRADE tools to evaluate the risk of bias and the certainty of evidence. At least two reviewers, working independently, selected the studies, extracted data and critically evaluated the evidence. We performed random-effects meta-analyses where possible. From 20 099 references initially identified, we included 6 randomised controlled trials (RCTs) with 616 participants. We considered the certainty of the body of evidence as low to very low regarding infection resolution after 6 weeks, risk of adverse events, mortality and clinical status. Based on six RCTs with considerable risk of bias, this systematic review found low or very low evidence about the advantages of intravenous antibiotic therapy at home. The imprecision of the results does not allow us to recommend or contraindicate the use of this strategy in clinical practice. [PROSPERO register: this review protocol was prospectively submitted at the PROSPERO register base at 31 January 2021 (submission no. 42021234454)]., (Copyright © 2022 Elsevier Ltd and International Society of Antimicrobial Chemotherapy. All rights reserved.)

Introduction: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques., Methods and Analysis: TRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation., Ethics and Dissemination: Ethical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications., Prospero Registration Number: CRD42019140361 and CRD42019161764., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Objectives: Case-control studies are often used to identify the risk factors for pancreatic cancer. The objective of this study was to evaluate the reporting of case-control studies of the risk factors for pancreatic cancer using the Strengthening The Reporting of OBservational Studies in Epidemiology (STROBE) for case-control studies checklist., Study Design and Setting: We conducted a comprehensive literature search of the MEDLINE and EMBASE databases to identify reports of case-control studies published between 2016 and 2018. We scored article reporting using a reporting adherence form developed from the STROBE checklist for case-control studies, consisting of 14 STROBE items related to the title, abstract, methods, and results sections., Results: We included reports of 47 case-control studies investigating a variety of risk factors, such as medical conditions and lifestyle factors. Reporting was inconsistent and inadequate. Efforts to address bias and how the study size was arrived at were particularly poorly described. Study cases were described in more detail than study controls., Conclusion: Reporting of case-control studies remains inadequate more than 10 years after the STROBE reporting guideline was published. Our findings suggest that authors do not understand the extent to which study methods and findings should be reported to enable studies to be fully understood, and their methods reproduced., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: To systematically assess the kind of placebos used in investigator-initiated randomized controlled trials (RCTs), from where they are obtained, and the hurdles that exist in obtaining them., Study Design and Setting: PubMed was searched for recently published noncommercial, placebo-controlled randomized drug trials. Corresponding authors were invited to participate in an online survey., Results: From 423 eligible articles, 109 (26%) corresponding authors (partially) participated. Twenty-one of 102 (21%) authors reported that the placebos used were not matching (correctly labeled in only one publication). The main sources in obtaining placebos were hospital pharmacies (32 of 107; 30%) and the manufacturer of the study drug (28 of 107; 26%). RCTs with a hypothesis in the interest of the manufacturer of the study drug were more likely to have obtained placebos from the drug manufacturer (18 of 49; 37% vs. 5 of 29; 17%). Median costs for placebos and packaging were US$ 58,286 (IQR US$ 2,428- US$ 160,770; n = 24), accounting for a median of 10.3% of the overall trial budget., Conclusion: Although using matching placebos is widely accepted as a basic practice in RCTs, there seems to be no standard source to acquire them. Obtaining placebos requires substantial resources, and using nonmatching placebos is common., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Competing Interests: Competing interest: All authors completed an ICMJE conflicts of interest form, available upon request from the corresponding author. All authors are involved, as part of the EQUATOR Network, in the development, update, implementation and dissemination of reporting guidelines.

Competing Interests: Gary Collins is involved in the TRIPOD Statement.

Context and Objective: The success of vaccination campaigns depends on the degree of adherence to immunization initiatives and schedules. Risk factors associated with children's failure to receive the measles vaccine at the correct age were studied in the city of São Paulo, Brazil., Design and Setting: Case-control and exploratory study, in the metropolitan area of São Paulo., Methods: The caregivers of 122 children were interviewed regarding their perceptions and understanding about the measles vaccination and the disease., Results: The results showed that age, region of residence, marital status and education level were unrelated to taking measles vaccines adequately. Most individuals remembered being informed about the last annual vaccination campaign by television, but no communication channel was significantly associated with vaccination status. The answers to questions about knowledge of the disease or the vaccine, when analyzed alone, were not associated with taking measles vaccinations at the time indicated by health agencies. The results showed that, when parents felt sorry for their children who were going to receive shots, they delayed the vaccination. Most of the children did not take the measles vaccination on the exactly recommended date, but delayed or anticipated the shots., Conclusion: It is clear that there is no compliance with the government's recommended measles vaccination schedule (i.e. first dose at nine and second at 15 months of age, as recommended in 1999 and 2000). Feeling sorry for the children receiving shots can delay vaccination taking.