The quality of randomized controlled trials of psychotherapy for eating disorders
Bibliographic record
Abstract
OBJECTIVE: We investigated the quality of randomized controlled trials (RCTs) that included direct comparisons of psychotherapy for adults with an eating disorder (ED). METHOD: Thirty-five direct comparison RCTs of psychotherapy for adults diagnosed with an ED were rated using the Randomized Controlled Trials Psychotherapy Quality Rating Scale (RCT-PQRS). RESULTS: The mean total RCT-PQRS score (mean = 28.26; SD = 7.04) was in line with those that were reported for RCTs of psychotherapy for depression and anxiety disorders. Several standards of quality were unfulfilled by over half of the RCTs of treatment for EDs, including therapist supervision while treatment was being provided (62.9% unfulfilled); outcome assessment performed by raters blind to treatment group/condition (54% unfulfilled); and adequate sample size (66% unfulfilled). More recent RCTs were of higher quality, and higher quality was moderately associated with lower effect sizes. CONCLUSIONS: To improve the quality of RCTs of psychotherapy of EDs, we recommend that researchers address the quality criteria listed in the RCT-PQRS. Psychotherapy trials should be registered, have a published protocol, and be reported following the Consolidated Standards of Reporting Trials guidelines. Authors should take into account the quality of the research when using that research to inform ED treatment guidelines.
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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 | 0.395 | 0.704 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".