Compliance of clinical trial registries with the World Health Organization minimum data set: a survey
Bibliographic record
Abstract
BACKGROUND: Since September 2005 the International Committee of Medical Journal Editors has required that trials be registered in accordance with the World Health Organization (WHO) minimum dataset, in order to be considered for publication. The objective is to evaluate registries' and individual trial records' compliance with the 2006 version of the WHO minimum data set. METHODS: A retrospective evaluation of 21 online clinical trial registries (international, national, specialty, pharmaceutical industry and local) from April 2005 to February 2007 and a cross-sectional evaluation of a stratified random sample of 610 trial records from the 21 registries. RESULTS: Among 11 registries that provided guidelines for registration, the median compliance with the WHO criteria were 14 out of 20 items (range 6 to 20). In the period April 2005-February 2007, six registries increased their compliance by six data items, on average. None of the local registry websites published guidelines on the trial data items required for registration. Slightly more than half (330/610; 54.1%, 95% CI 50.1% - 58.1%) of trial records completed the contact details criteria while 29.7% (181/610, 95% CI 26.1% - 33.5%) completed the key clinical and methodological data fields. CONCLUSION: While the launch of the WHO minimum data set seemed to positively influence registries with better standardisation of approaches, individual registry entries are largely incomplete. Initiatives to ensure quality assurance of registries and trial data should be encouraged. Peer reviewers and editors should scrutinise clinical trial registration records to ensure consistency with WHO's core content requirements when considering trial-related publications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.306 | 0.572 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".