Intention-to-Treat Analysis in Clinical Research
Bibliographic record
Abstract
ABSTRACT: This review presents a comprehensive summary and critical evaluation of intention-to-treat analysis, with a particular focus on its application to randomized controlled trials within the field of rehabilitation. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a methodological review that encompassed electronic and manual search strategies to identify relevant studies. Our selection process involved two independent reviewers who initially screened titles and abstracts and subsequently performed full-text screening based on established eligibility criteria. In addition, we included studies from manual searches that were already cataloged within the first author's personal database. The findings are synthesized through a narrative approach, covering fundamental aspects of intention to treat, including its definition, common misconceptions, advantages, disadvantages, and key recommendations. Notably, the health literature offers a variety of definitions for intention to treat, which can lead to misinterpretations and inappropriate application when analyzing randomized controlled trial results, potentially resulting in misleading findings with significant implications for healthcare decision making. Authors should clearly report the specific intention-to-treat definition used in their analysis, provide details on participant dropouts, and explain upon their approach to managing missing data. Adherence to reporting guidelines, such as the Consolidated Standards of Reporting Trials for randomized controlled trials, is essential to standardize intention-to-treat information, ensuring the delivery of accurate and informative results for healthcare decision making.
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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 | 0.513 | 0.744 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.018 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".