Discrepancies between registered and published primary outcome specifications in analgesic trials: ACTTION systematic review and recommendations
Bibliographic record
Abstract
The National Institutes of Health released the trial registry ClinicalTrials.gov in 2000 to increase public reporting and clinical trial transparency. This systematic review examined whether registered primary outcome specifications (POS; ie, definitions, timing, and analytic plans) in analgesic treatment trials correspond with published POS. Trials with accompanying publications (n = 87) were selected from the Repository of Registered Analgesic Clinical Trials (RReACT) database of all postherpetic neuralgia, diabetic peripheral neuropathy, and fibromyalgia clinical trials registered at ClinicalTrials.gov as of December 1, 2011. POS never matched precisely; discrepancies occurred in 79% of the registry-publication pairs (21% failed to register or publish primary outcomes [PO]). These percentages did not differ significantly between industry and non-industry-sponsored trials. Thirty percent of the trials contained unambiguous POS discrepancies (eg, omitting a registered PO from the publication, "demoting" a registered PO to a published secondary outcome), with a statistically significantly higher percentage of non-industry-sponsored than industry-sponsored trials containing unambiguous POS discrepancies. POS discrepancies due to ambiguous reporting included vaguely worded PO registration; or failing to report the timing of PO assessment, statistical analysis used for the PO, or method to address missing PO data. At best, POS discrepancies may be attributable to insufficient registry requirements, carelessness (eg, failing to report PO assessment timing), or difficulty uploading registry information. At worst, discrepancies could indicate investigator impropriety (eg, registering imprecise PO ["pain"], then publishing whichever pain assessment produced statistically significant results). Improvements in PO registration, as well as journal policies requiring consistency between registered and published PO descriptions, are needed.
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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 | 0.659 | 0.849 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.013 | 0.010 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".