A scoping review on two-stage randomized preference trial in mental health
Bibliographic record
Abstract
Abstract Background : Randomized Controlled Trial is the most rigorous study design to test the efficacy and effectiveness of an intervention. Patient preference may negatively affect patient performance and decrease the generalizability of a trial to clinical population. Patient preference trial have particular implications in mental health since mental health interventions are generally complex, blinding of intervention is often difficult or impossible, patients may have strong preference, and outcome measures are often subjective patient self-report which may be greatly influenced if patient’s preference did not match with the intervention received. Methods: In this review, we have surveyed the application of two-stage randomized preference trial with focus on studies in the field of mental health. The study selection followed guideline provided by Preferred Reporting Items for Systematic Reviews and Meta-Analysis. Results: Six two-stage randomized preference trials (ten publications) have been identified in the mental health field. In these trials, the pooled dropout rates were 18.3% for the preference arm, and 28.7% for the random arm, with a pooled RR of 0.70 (95% CI, 0.56-0.88; P=0.010) indicating significantly lower risk of dropout in the preference arm. The standardized preference effects varied widely from 0.07 to 0.57, and could be as large as the treatment effect in some of the trials. Conclusion: This scoping review has shown that two-stage randomized preference trials are not as popular as expected in mental health research. The results indicated that two-stage randomized preference trials in mental health would be beneficial in retaining patients to expand the generalizability of the trial.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.125 | 0.470 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".