Comparing treatments for post-traumatic stress disorder - a systematic review.
Bibliographic record
Abstract
INTRODUCTION: The recommended treatments for post-traumatic stress disorder (PTSD) are psychological therapies and medication, but the best approach is still discussed. Exposure to traumatic events in psychotherapy tends to cause high drop-out rates. Likewise, little effect or adverse events of medications may lead to attrition. The aim of this study was to compare the outcomes of treatment by psychotherapy and medications. An additional aim was to explore the combinations of treatment modalities in adults with PTSD and to investigate differences in drop-out rates. METHODS: A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. PubMed and Embase were searched for relevant randomised clinical trials. The Cochrane risk-of-bias tool was used to assess the quality of the retrieved trials. RESULTS: Seven eligible studies were identified. Three studies showed that psychotherapy was superior to selective serotonin reuptake inhibitors. Two studies showed an augmenting effect with prolonged exposure. Two studies showed no differences across the treatment groups. In four of the included studies, patients treated with psychotherapy were more likely to drop out. CONCLUSIONS: Extant evidence is insufficient to assess whether combined therapy is superior to monotherapy. Both medication and psychotherapy have an effect on PTSD, but psychotherapy tends to provide greater and more long-lasting outcome improvements. Trauma type, PTSD severity and other variables affect drop-out rates and treatment outcomes.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".