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The methods and results of health research are documented in study protocols, full study reports (detailing all analyses), journal reports, and participant-level datasets. However, protocols, full study reports, and participant-level datasets are rarely available, and journal reports are available for only half of all studies and are plagued by selective reporting of methods and results. Furthermore, information provided in study protocols and reports varies in quality and is often incomplete. When full information about studies is inaccessible, billions of dollars in investment are wasted, bias is introduced, and research and care of patients are detrimentally affected. To help to improve this situation at a systemic level, three main actions are warranted. First, academic institutions and funders should reward investigators who fully disseminate their research protocols, reports, and participant-level datasets. Second, standards for the content of protocols and full study reports and for data sharing practices should be rigorously developed and adopted for all types of health research. Finally, journals, funders, sponsors, research ethics committees, regulators, and legislators should endorse and enforce policies supporting study registration and wide availability of journal reports, full study reports, and participant-level datasets.

AbstractThe SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition that interventions involving artificial intelligence need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes.The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI. Both guidelines were developed using a staged consensus process, involving a literature review and expert consultation to generate 26 candidate items, which were consulted on by an international multi-stakeholder group in a 2-stage Delphi survey (103 stakeholders), agreed on in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants).The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations around the handling of input and output data, the human-AI interaction and analysis of error cases.SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

Global biomedical and public health research involves billions of dollars and millions of people. Although this vast enterprise has led to substantial health improvements, many more gains are possible if the waste and inefficiency in the ways that biomedical research is chosen, designed, done, analysed, regulated, managed, disseminated, and reported can be addressed.

Systematic reviews of clinical trials aim to include all relevant studies conducted on a particular topic and to provide an unbiased summary of their results, producing the best evidence about the benefits and harms of medical treatments. Relevant studies, however, may not provide the results for all measured outcomes or may selectively report only some of the analyses undertaken, leading to unnecessary waste in the production and reporting of research, and potentially biasing the conclusions to systematic reviews. In this article, Kirkham and colleagues provide a methodological approach, with an example of how to identify missing outcome data and how to assess and adjust for outcome reporting bias in systematic reviews.

To describe, and explain the rationale for, the methods used and decisions made during development of the updated SPIRIT 2024 and CONSORT 2024 reporting guidelines. We developed SPIRIT 2024 and CONSORT 2024 together to facilitate harmonization of the two guidelines, and incorporated content from key extensions. We conducted a scoping review of comments suggesting changes to SPIRIT 2013 and CONSORT 2010, and compiled a list of other possible revisions based on existing SPIRIT and CONSORT extensions, other reporting guidelines, and personal communications. From this, we generated a list of potential modifications or additions to SPIRIT and CONSORT, which we presented to stakeholders for feedback in an international online Delphi survey. The Delphi survey results were discussed at an online expert consensus meeting attended by 30 invited international participants. We then drafted the updated SPIRIT and CONSORT checklists and revised them based on further feedback from meeting attendees. We compiled 83 suggestions for revisions or additions to SPIRIT and/or CONSORT from the scoping review and 85 from other sources, from which we generated 33 potential changes to SPIRIT (n = 5) or CONSORT (n = 28). Of 463 participants invited to take part in the Delphi survey, 317 (68%) responded to Round 1, 303 (65%) to Round 2 and 290 (63%) to Round 3. Two additional potential checklist changes were added to the Delphi survey based on Round 1 comments. Overall, 14/35 (SPIRIT n = 0; CONSORT n = 14) proposed changes reached the predefined consensus threshold (≥80% agreement), and participants provided 3580 free-text comments. The consensus meeting participants agreed with implementing 11/14 of the proposed changes that reached consensus in the Delphi and supported implementing a further 4/21 changes (SPIRIT n = 2; CONSORT n = 2) that had not reached the Delphi threshold. They also recommended further changes to refine key concepts and for clarity. The forthcoming SPIRIT 2024 and CONSORT 2024 Statements will provide updated, harmonized guidance for reporting randomized controlled trial protocols and results, respectively. The simultaneous development of the SPIRIT and CONSORT checklists has been informed by current empirical evidence and extensive input from stakeholders. We hope that this report of the methods used will be helpful for developers of future reporting guidelines.

Lack of ethnic diversity in trials may contribute to health disparities and to inequity in health outcomes. The primary objective was to investigate the experiences and perspectives of ethnically diverse populations about how to improve ethnic diversity in trials. Qualitative data were collected via 16 focus groups with participants from 21 ethnically diverse communities in Australia. Data collection took place between August and September 2022 in community-based settings in six capital cities: Sydney, Melbourne, Perth, Adelaide, Brisbane, and Darwin, and one rural town: Bordertown (South Australia). One hundred and fifty-eight purposively sampled adults (aged 18–85, 49% women) participated in groups speaking Tamil, Greek, Punjabi, Italian, Mandarin, Cantonese, Karin, Vietnamese, Nepalese, and Arabic; or English-language groups (comprising Fijian, Filipino, African, and two multicultural groups). Only 10 participants had previously taken part in medical research including three in trials. There was support for medical research, including trials; however, most participants had never been invited to participate. To increase ethnic diversity in trial populations, participants recommended recruitment via partnering with communities, translating trial materials and making them culturally accessible using audiovisual ways, promoting retention by minimizing participant burden, establishing trust and rapport between participants and researchers, and sharing individual results. Participants were reluctant to join studies on taboo topics in their communities (eg, sexual health) or in which physical specimens (eg, blood) were needed. Participants said these barriers could be mitigated by communicating about the topic in more culturally cognizant and safe ways, explaining how data would be securely stored, and reinforcing the benefit of medical research to humanity. Participants recognized the principal benefits of trials and other medical research, were prepared to take part, and offered suggestions on recruitment, consent, data collection mechanisms, and retention to enable this to occur. Researchers should consider these community insights when designing and conducting trials; and government, regulators, funders, and publishers should allow for greater innovation and flexibility in their processes to enable ethnic diversity in trials to improve. [Display omitted]

To update SPIRIT 2013 and CONSORT 2010 reporting guidelines. For SPIRIT 2025, a comprehensive review process incorporating scoping reviews, expert consultations, and a Delphi survey led to a revised checklist. CONSORT 2025 underwent a similar update, addressing methodological advancements and user feedback gathered through scoping reviews, expert consultations, and a Delphi survey. Key changes include the addition of two new items, revisions to five, and the deletion or merging of five, alongside a new Open Science section. Emphasis on harm assessment, intervention description, and patient/public involvement has also been strengthened. The SPIRIT 2025 statement provides a 34-item checklist, a schedule diagram, and an expanded checklist with explanations. Similarly, seven new items were added, three revised, and one deleted, with content integrated from existing CONSORT extensions. A new Open Science section was also incorporated. The CONSORT 2025 statement offers a 30-item checklist, a flow diagram, and a detailed explanatory checklist. Both updated statements and explanatory articles aim to enhance transparency and completeness in trial protocols and reporting. Widespread adoption by investigators, funders, ethics committees, journals, and regulators should improve the quality and usability of research, ultimately benefiting patients and others. We have updated two important guidelines, SPIRIT and CONSORT, to make research studies easier to understand and more reliable. We did this by carefully reviewing existing research and getting feedback from experts around the world. The new SPIRIT 2025 guideline has 34 items to help researchers report their study plans clearly. It includes new sections on how to assess potential risks, describe the treatment, and involve patients in the research. We also added a new section about sharing data openly. The new CONSORT 2025 guideline has 30 items to help researchers report the results of their studies. It includes new sections on harms, outcomes, nondrug treatments, and how treatments are tailored to individuals. Like SPIRIT 2025, it also has a new section on open science. We have also created detailed guides to help people use these new guidelines. We hope that by using these updated guidelines, researchers, funders, ethics committees, journals, and regulators will help improve the quality of research and ultimately benefit patients. •Accurate interpretation of randomized trials requires complete and transparent reporting of methods and findings.•The SPIRIT 2025 statement updates guidance on trial protocol content, incorporating methodological advances and user feedback.•SPIRIT 2025 includes three key components for trial protocol guidance: a 34-item checklist, a schedule diagram, and an expanded checklist with detailed explanations.•For reporting randomized trials, the CONSORT 2025 Statement includes a 30-item checklist, a flow diagram, and a detailed explanation of each checklist item.

Background The application of artificial intelligence (AI) in healthcare is an area of immense interest. The high profile of ‘AI in health’ means that there are unusually strong drivers to accelerate the introduction and implementation of innovative AI interventions, which may not be supported by the available evidence, and for which the usual systems of appraisal may not yet be sufficient. Main text We are beginning to see the emergence of randomised clinical trials evaluating AI interventions in real-world settings. It is imperative that these studies are conducted and reported to the highest standards to enable effective evaluation because they will potentially be a key part of the evidence that is used when deciding whether an AI intervention is sufficiently safe and effective to be approved and commissioned. Minimum reporting guidelines for clinical trial protocols and reports have been instrumental in improving the quality of clinical trials and promoting completeness and transparency of reporting for the evaluation of new health interventions. The current guidelines—SPIRIT and CONSORT—are suited to traditional health interventions but research has revealed that they do not adequately address potential sources of bias specific to AI systems. Examples of elements that require specific reporting include algorithm version and the procedure for acquiring input data. In response, the SPIRIT-AI and CONSORT-AI guidelines were developed by a multidisciplinary group of international experts using a consensus building methodological process. The extensions include a number of new items that should be reported in addition to the core items. Each item, where possible, was informed by challenges identified in existing studies of AI systems in health settings. Conclusion The SPIRIT-AI and CONSORT-AI guidelines provide the first international standards for clinical trials of AI systems. The guidelines are designed to ensure complete and transparent reporting of clinical trial protocols and reports involving AI interventions and have the potential to improve the quality of these clinical trials through improvements in their design and delivery. Their use will help to efficiently identify the safest and most effective AI interventions and commission them with confidence for the benefit of patients and the public.

Abbreviations: FDA, Food and Drug Administration; NDA, new drug application Provenance: Commissioned; not externally peer reviewed Although randomized trials provide key guidance for how we practice medicine, trust in their published results has been eroded in recent years due to several high-profile cases of alleged data suppression, misrepresentation, and manipulation [1-5, 39]. [...]only devices, pharmaceuticals, and biological agents require regulatory approval in the United States and other countries, meaning that trials examining other types of interventions (e.g., surgery, education)--which constitute 20% of published randomized trials [24]--would be excluded from reviews of regulatory agency documents.

ObjectivesTo study the prevalence and characteristics of outcome switching, the completeness of outcome prespecification, and factors associated with outcome switching in observational cohort studies of interventions.DesignLongitudinal meta-epidemiological study.SettingRegistry records and journal publications.ParticipantsControlled cohort studies investigating the effects of interventions. Eligible studies were registered on ClinicalTrials.gov within one month of their start date (2014-16) and had published results in peer reviewed journals by 2024.Main outcomes measuresFirstly, proportion of studies with outcome switching identified by comparing the prespecified outcomes in the registry and those reported in the journal publication of results. Discrepancies were categorised as omission (prespecified primary outcomes not reported), downgrading (prespecified primary outcomes reported as non-primary), upgrading (prespecified non-primary outcomes reported as primary), and introduction of new primary outcomes (not registered as an outcome). Secondly, proportion of studies with completely prespecified primary outcomes, defined as registry entries that include the measurement variable, analysis metric, method of aggregation (the statistic summarising the outcome within each study group), and time point.ResultsOf 9965 registration records screened, 124 eligible studies with results published between 2015 and 2024 were included. Only 30 studies (24%) completely prespecified their primary outcomes. Outcome switching occurred in 60 (48%) studies, but only two provided an explanation. The most common types of switching were omission (n=32, 26%) and downgrading (n=32, 26%), followed by the introduction of new primary outcomes (n=25, 20%), and upgrading (n=2, 2%). Among 57 studies with outcome switching other than omission (ie, outcome results were reported), statistically significant results were favoured in 77% (44/57) by introducing or upgrading a new significant primary outcome or downgrading a non-significant one. No study characteristics were significantly associated with outcome switching in multivariable logistic regression.ConclusionsOutcome switching and inadequate outcome prespecification were common in cohort studies of interventions. Most changes were unexplained and favoured statistically significant results, raising concerns about potential selective reporting and highlighting the need for improved transparency in outcome reporting.Study registrationOpen Science Framework (https://osf.io/xn5zt/).