Considerations in selecting comparison conditions in psychotherapy trials: Recommendations for future research.
Bibliographic record
Abstract
OBJECTIVE: In this commentary, we outline conceptual and methodological concerns we have with a recent randomized trial of two group-delivered transdiagnostic eating disorder treatments (Stice et al., 2023), particularly regarding the description, implementation, and labeling of the comparison condition. METHOD: We discuss the selection of a control condition in comparative psychotherapy trials; the distinction between adaptations and other types of intervention modifications; the need for processes to ensure that an intervention is developmentally and diagnostically appropriate; and the provision of detailed descriptions of interventions in articles and supplementary materials, as well as making manuals publicly available, to ensure that reviewers and readers can understand the interventions delivered and can accurately interpret the results. RESULTS: We highlight the potential downstream implications of mislabeling an intervention and conclude that the comparison condition in Stice et al.'s (2023) article should be reclassified to avoid misinterpretation. CONCLUSIONS: There are published frameworks and guidelines available that promote more detail, precision, and transparency about interventions being tested in clinical trials. We believe it is time for journals to implement these guidelines to ensure that reviewers and readers can fully understand what interventions were tested to draw informed conclusions from the study, replicate research findings, and reliably deliver these interventions in clinical practice. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.716 | 0.885 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.018 | 0.007 |
| Research integrity | 0.034 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".