Comparison of published orthopaedic trauma trials following registration in Clinicaltrials.gov
Bibliographic record
Abstract
BACKGROUND: After the Food and Drug Administration Modernization Act of 1997, the registration of all clinical trials became mandatory prior to publication. Our primary objective was to determine publication rates for orthopaedic trauma trials registered with ClinicalTrials.gov. We further evaluated methodological consistency between registration and publication. METHODS: We searched Clinical Trials.gov for all trials related to orthopaedic trauma. We excluded active trials and trials not completed by July 2009, and performed a systematic search for publications resulting from registered closed trials. Information regarding primary and secondary outcomes, intervention, study sponsors, and sample size were extracted from registrations and publications. RESULTS: Of 130 closed trials, 37 eligible trials resulted in 16 publications (43.2%). We found no significant differences in publication rates between funding sources for industry sponsored studies and nongovernment/nonindustry sponsored studies (p > 0.05). About half the trials (45%) did not include the NCT ID in the publication. Two (10%) publications had major changes to the primary outcome measure and ten (52.6%) to sample size. CONCLUSIONS: Registration of orthopaedic trauma trials does not consistently result in publication. When trials are registered, many do not cite NCT ID in the publication. Furthermore, changes that are not reflected in the registry of the trial are frequently made to the final publication.
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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 | 0.296 | 0.670 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.029 | 0.043 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".