Reporting of intention-to-treat analyses in recent analgesic clinical trials: ACTTION systematic review and recommendations
Bibliographic record
Abstract
The intention-to-treat (ITT) principle states that all subjects in a randomized clinical trial (RCT) should be analyzed in the group to which they were assigned, regardless of compliance with assigned treatment. Analyses performed according to the ITT principle preserve the benefits of randomization and are recommended by regulators and statisticians for analyses of RCTs. The objective of this study was to determine the frequency with which publications of analgesic RCTs in 3 major pain journals report an ITT analysis and the percentage of the author-declared ITT analyses that include all randomized subjects and thereby fulfill the most common interpretation of the ITT principle. RCTs investigating noninvasive, pharmacologic and interventional (eg, nerve blocks, implantable pumps, spinal cord stimulators, surgery) treatments for pain, published between January 2006 and June 2013 (n=173), were included. None of the trials using experimental pain models reported an ITT analysis; 47% of trials investigating clinical pain conditions reported an ITT analysis, and 5% reported a modified ITT analysis. Of the analyses reported as ITT, 67% reported reasons for excluding subjects from the analysis, and 18% of those listing reasons for exclusion did not do so in the Methods section. Such mislabeling can make it difficult to identify traditional ITT analyses for inclusion in meta-analyses. We hope that deficiencies in reporting identified in this study will encourage authors, reviewers, and editors to promote more consistent use of the term "intention to treat" for more accurate reporting of RCT-based evidence for pain treatments.
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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 | 0.733 | 0.892 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.019 | 0.026 |
| Bibliometrics | 0.037 | 0.032 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".