The Unsolved Problem of Attrition Rates on Randomized Clinical Trials for Cocaine Use Disorders: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Cocaine use disorder (CUD) is a significant and insufficiently studied public health issue, especially considering that the global prevalence of CUD is estimated to be higher than ever. There is still no consensus on effective treatments for CUD. Important barriers for research in the field include the high attrition levels observed in randomized controlled trials (RCTs) for CUD treatment and the lack of emphasis on methods to reduce attrition in CUD RCTs. METHODS: The goal of this study was to systematically review over 2 decades of CUD RCTs, with the objective of evaluating the reporting of attrition bias and methods used to mitigate attrition. RESULTS: Our scoping review extracted information from 106 RCTs, of which only 82 explicitly evaluated attrition as an outcome. Thirty-eight studies had an attrition rate above 50%, and five 16 studies had medium attrition bias, 6% to 19%. The remaining 68 had large attrition bias. CONCLUSION: Across all included studies, discussion of attrition as a limitation was uncommon. Overall, these analyses suggest that most RCTs evaluating CUD treatments have not adequately accounted for attrition in their analyses or employed approaches to mitigate attrition.
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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 | 0.531 | 0.800 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| 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".