Attrition, missing data, compliance, and related biases in randomized controlled trials of rehabilitation interventions: towards improving reporting and conduct
Bibliographic record
Abstract
INTRODUCTION: Attrition, missing data, compliance, and related biases can influence the magnitude of treatment effects in randomized controlled trials (RCTs). It is unclear which items should be considered when reporting and evaluating the influence of these biases in trial reports in the rehabilitation field. The aim was to describe which individual items considering attrition, missing data, compliance, and related biases are included in quality tools used in rehabilitation research. In addition, we aimed to determine whether the existing reporting guidelines, such as the CONSORT and its extensions include all relevant items related to these biases when reporting RCTs in the area of rehabilitation. EVIDENCE ACQUISITION: Comprehensive literature searches and a systematic approach to identify tools and items looking at attrition, missing data, compliance and related biases in rehabilitation were performed. We extracted individual items linked to these biases from all quality tools. We calculated the frequency of quality items used across tools and compared them to those found in the CONSORT statement and its extensions. A list of items to be potentially added to the CONSORT statement was generated. EVIDENCE SYNTHESIS: Three new tools to assess the conduct and reporting of trials in the rehabilitation field were found. From these tools, 28 items were used to evaluate the reporting as well as the conduct of trials considering attrition, missing data, compliance, and related biases in the rehabilitation field. However, our team found that some of these items lack specificity in the information required and therefore more research is needed to determine a core set of items used for reporting as well as assessing the risk of bias (RoB) of RCT in the rehabilitation field. CONCLUSIONS: Although many items have been described by existing tools and the CONSORT statement (and its extensions) that deal with attrition, missing data, compliance, and related biases, several gaps in reporting were identified. It is crucial that future research investigate a core set of items to be used in the field of rehabilitation to facilitate the reporting as well as the conduct of RCTs.
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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 | 0.728 | 0.882 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.016 | 0.026 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".