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Accessibility of clinical study reports supporting medicine approvals: a cross-sectional evaluation

2024· article· en· W4390858342 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Clinical Epidemiology · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersCancer Council South AustraliaNational Health and Medical Research CouncilMedical Research Council
KeywordsCross-sectional studyMedicineMEDLINEFamily medicineEnvironmental healthPathologyPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: Clinical study reports (CSRs) are highly detailed documents that play a pivotal role in medicine approval processes. Though not historically publicly available, in recent years, major entities including the European Medicines Agency (EMA), Health Canada, and the US Food and Drug Administration (FDA) have highlighted the importance of CSR accessibility. The primary objective herein was to determine the proportion of CSRs that support medicine approvals available for public download as well as the proportion eligible for independent researcher request via the study sponsor. STUDY DESIGN AND SETTING: This cross-sectional study examined the accessibility of CSRs from industry-sponsored clinical trials whose results were reported in the FDA-authorized drug labels of the top 30 highest-revenue medicines of 2021. We determined (1) whether the CSRs were available for download from a public repository, and (2) whether the CSRs were eligible for request by independent researchers based on trial sponsors' data sharing policies. RESULTS: There were 316 industry-sponsored clinical trials with results presented in the FDA-authorized drug labels of the 30 sampled medicines. Of these trials, CSRs were available for public download from 70 (22%), with 37 available at EMA and 40 at Health Canada repositories. While pharmaceutical company platforms offered no direct downloads of CSRs, sponsors confirmed that CSRs from 183 (58%) of the 316 clinical trials were eligible for independent researcher request via the submission of a research proposal. Overall, 218 (69%) of the sampled clinical trials had CSRs available for public download and/or were eligible for request from the trial sponsor. CONCLUSION: CSRs were available from 69% of the clinical trials supporting regulatory approval of the 30 medicines sampled. However, only 22% of the CSRs were directly downloadable from regulatory agencies, the remaining required a formal application process to request access to the CSR from the study sponsor.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reporting · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearchScholarly communicationOpen science
Domain: Reproducibility · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.964
metaresearch head score (Gemma)0.967
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9640.967
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0190.008
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0130.000

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 head0.975
GPT teacher head0.809
Teacher spread0.167 · 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