Risk of Bias in Randomized Clinical Trials on Psychological Therapies for Post-Traumatic Stress Disorder in Adults
Bibliographic record
Abstract
OBJECTIVE: To evaluate the factorial validity and internal consistency of a measurement model underlying risk of bias as endorsed by Cochrane for use in systematic reviews; more specifically, how the risk of bias tool behaves in the context of studies on psychological therapies used for treatment of post-traumatic stress disorder in adults. METHODS: We applied confirmatory factor analysis to a systematic review containing 70 clinical trials entitled "Psychological Therapies for Chronic Post-Traumatic Stress Disorder in Adults" under a Bayesian estimator. Seven observed categorical risk of bias items (answered categorically as low, unclear, or high risk of bias) were collected from the systematic review. RESULTS: A unidimensional model for the Cochrane risk of bias tool items returned poor fit indices and low factor loadings, indicating questionable validity and internal consistency. CONCLUSION: Although the present evidence is restricted to psychological interventions for post-traumatic stress disorder, it demonstrates that the way risk of bias has been measured in this context may not be adequate. More broadly, the results suggest the importance of testing the risk of bias tool, and the possibility of rethinking the methods used to assess risk of bias in systematic reviews and meta-analyses.
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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 | 0.546 | 0.823 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| 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; 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".