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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.)

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.)

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.)