Publication Rate and Consistency of Registered Trials of Motor-Based Stroke Rehabilitation
Bibliographic record
Abstract
OBJECTIVE: To determine the publication rate of motor-rehabilitation trials poststroke and the consistency between registry records and their corresponding main publications in trial design, primary objectives and outcomes, eligibility criteria, and sample size. METHODS: We searched 18 clinical trial registries to identify randomized controlled trials of motor-based stroke rehabilitation registered after July 2005 and completed before April 2017. Eligible trials included adults with stroke, with at least one outcome measure related to motor function. Information in the registry records was compared with that of their main publications, if any. RESULTS: Three hundred twenty-three trials met our eligibility criteria; we were unable to find a peer-reviewed publication reporting the main findings for 46% (150/323) of these. Of the 169 trials with peer-reviewed articles published in English, 141 (83%) were consistent with the registry record in trial design, 100 (59%) were consistent in primary objectives, 71 (42%) were consistent in primary outcomes, 28 (17%) were consistent in eligibility criteria, and 74 (44%) were consistent in sample size. CONCLUSIONS: Approximately half of motor-based stroke rehabilitation trials were not published, even more than 3 years after the end of the trial. When main publications were found, they substantially deviated from information in the registry record. These findings highlight the importance of trial registries for identifying unpublished stroke rehabilitation trials and of searching trial registries when conducting systematic reviews and meta-analysis to help ensure that reviews are unbiased.
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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 | 0.542 | 0.844 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.031 | 0.034 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".