MétaCan
Menu
← Back to cohort
Record W4362700808 · doi:10.1101/2023.04.05.23288169

Are European Clinical Trial Funders Policies on Clinical Trial Registration and Reporting Improving? – A Cross-Sectional Study

2023· preprint· en· W4362700808 on OpenAlexfundaboutno aff
Marguerite O’Riordan, Martin Haslberger, Carolina Cruz, Tarik Suljić, Martin Ringsten, Till Brückner

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
FundersEuropean and Developing Countries Clinical Trials PartnershipHealth Research Council of New ZealandIndian Council of Medical ResearchMedical Research CouncilBlood Cancer UKVlaamse regeringVetenskapsrådetMinistero della SaluteSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDeutsche ForschungsgemeinschaftEuropean CommissionBundesministerium für Bildung und ForschungNational Institute for Health and Care ResearchBritish Heart FoundationFonds Wetenschappelijk OnderzoekCanadian Institutes of Health ResearchNational Science FoundationNational Health and Medical Research CouncilCancer Research UKInstituto de Salud Carlos IIIAustrian Science FundZonMwInstitut National de la Santé et de la Recherche MédicaleBowel Cancer UK
KeywordsDocumentationBest practicePsychological interventionFamily medicineMedicineClinical trialClinical PracticeMEDLINEPolitical scienceMedical educationNursing

Abstract

fetched live from OpenAlex

Abstract Objectives Assess the extent to which the clinical trial registration and reporting policies of 25 of the world’s largest public and philanthropic medical research funders meet best practice benchmarks as stipulated by the 2017 WHO Joint Statement,(1) and document changes in the policies and monitoring systems of 19 European funders over the past year. Design, Setting, Participants Cross sectional study, based on assessments of each funder’s publicly available documentation plus validation of results by funders. Our cohort includes the 25 of the largest public and philanthropic medical research funders in Europe, Oceania, South Asia and Canada. Of these, 19 were previously assessed against the same benchmarks, enabling us to document changes over time. Interventions Scoring of all 25 funders using an 11-item assessment tool based on WHO best practice benchmarks, grouped into 3 primary categories: trial registries, academic publication and monitoring, plus validation of results by funders. Main outcome measures The primary outcome measure is how many of the 11 WHO best practice items each of the 25 funders has put into place, and changes in the performance of 19 previously assessed funders over the preceding year. Results The 25 funders we assessed had put into place an average of 5/11 (49%) WHO best practices. The best practice adopted by most funders 16/25 (64%) was mandating open access publication in journals. In contrast, only 6/25 funders (24%) took PI’s past reporting record into account during grant application reviews. Funders’ performance varied widely from 0/11 to 11/11 WHO best practices adopted. Of the 19 funders for which 2021 baseline data were available,(2) 10/19 (53%) had strengthened their policies over the preceding year. Conclusions Most medical research funders need to do more to curb research waste and publication bias by strengthening their clinical trial policies. Key Points WHAT IS ALREADY KNOWN ABOUT THIS TOPIC Strong clinical trial registration and reporting policies coupled with monitoring and sanctions can reduce research waste, curb publication bias and promote transparency. A 2021 assessment found that 19 European medical research funders’ policies fell short of WHO best practices. WHAT THIS STUDY ADDS This is the first study to assess the clinical trial registration and reporting policies of a global cohort of 25 major medical research funders against WHO best practices, identifying gaps in the research waste safeguards of key players across Europe, Oceania, South Asia and Canada. In addition, the study assesses the progress made by 19 funders in the recent past. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY This study enables funders worldwide to identify and address gaps in their clinical trial transparency policies by pinpointing exactly where they currently fall short of WHO best practices. It also enables policy makers and citizens to assess whether public bodies tasked with furthering medical knowledge have adopted adequate safeguards against research waste and publication bias.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.119
metaresearch head score (Gemma)0.174
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.627

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1190.174
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.002
Scholarly communication0.0050.006
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.872
GPT teacher head0.606
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainReporting
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuemedRxiv→Same topicHealth Systems, Economic Evaluations, Quality of Life→French-language works237,207→