Factors related to dropout in integrative oncology clinical trials: interim analysis of an ongoing comparative effectiveness trial of mindfulness-based cancer recovery and Tai chi/Qigong for cancer health (The MATCH study)
Bibliographic record
Abstract
OBJECTIVE: To examine the factors associated with loss to follow-up (LTFU) in an ongoing preference-based randomized waitlist controlled trial of mindfulness-based cancer recovery (MBCR) and Taichi/Qigong (TCQ) for cancer survivors (the MATCH Study). Hierarchical logistic regression was used to determine the factors associated with LTFU. Predictors included adherence to treatment, preference vs. randomized, type of intervention (MBCR vs. TCQ) and program timing (immediate {IM} vs. waitlist control {WLC} group). RESULTS: Data indicated that randomization to the WLC group and, once in the intervention, low adherence were the main predictors of LTFU. Participants in the WLC group were 4 times more likely to be LTFU post-randomization [OR 3.96, 95% CI 2.08-7.56, p < 0.005] than those in the IM group. Participants showing low adherence to treatment were 6 times more likely for LTFU post-intervention [5.87 (2.57-13.400; p < 0.005] and 4 times more likely for LTFU 6 months post-intervention [OR 3.93, 95% CI 1.53-10.02, p = 0.01].
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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 | 0.069 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".