Reporting of cross-over clinical trials of analgesic treatments for chronic pain: Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks systematic review and recommendations
Bibliographic record
Abstract
Cross-over trials are typically more efficient than parallel group trials in that the sample size required to yield a desired power is substantially smaller. It is important, however, to consider some issues specific to cross-over trials when designing and reporting them, and when evaluating the published results of such trials. This systematic review evaluated the quality of reporting and its evolution over time in articles of cross-over clinical trials of pharmacologic treatments for chronic pain published between 1993 and 2013. Seventy-six (61%) articles reported a within-subject primary analysis, or if no primary analysis was identified, reported at least 1 within-subject analysis, which is required to achieve the gain in power associated with the cross-over design. For 39 (31%) articles, it was unclear whether analyses conducted were within-subject or between-group. Only 36 (29%) articles reported a method to accommodate missing data (eg, last observation carried forward, n = 29), and of those, just 14 included subjects in the analysis who provided data from only 1 period. Of the articles that identified a within-subject primary analysis, 21 (51%) provided sufficient information for the results to be included in a meta-analysis (ie, estimates of the within-subject treatment effect and variability). These results and others presented in this article demonstrate deficiencies in reporting of cross-over trials for analgesic treatments. Clearer reporting in future trials could improve readers' ability to critically evaluate the results, use these data in meta-analyses, and plan future trials. Recommendations for proper reporting of cross-over trials that apply to any condition are provided.
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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 | 0.604 | 0.865 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".