Management of post-traumatic stress disorder: A protocol for a multiple treatment comparison meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Background: Most systematic reviews have explored the efficacy of treatments on symptoms associated with post-traumatic stress disorder (PTSD), which is a chronic and often disabling condition.Previous network meta-analysis (NMA) had limitations such as focusing on pharmacological or psychotherapies.Our review is aims to explore the relative effectiveness of both pharmacological and psychotherapies and we will establish the differential efficacy of interventions for PTSD in consideration of both symptom reduction and functional recovery.Methods: We will conduct a network meta-analysis of randomized controlled trials evaluating treatment interventions for PTSD.We will systematically search Medline, PILOT, Embase, CINHAL, AMED, Psychinfo, Health Star, DARE and CENTRAL to identify trials that: (1) enroll adult patients with PTSD, and (2) randomize them to alternative interventions or an intervention and a placebo/sham arm.Independent reviewers will screen trials for eligibility, assess risk of bias using a modified Cochrane instrument, and extract data.Our outcomes of interest include PTSD symptom reduction, quality of life, functional recovery, social and occupational impairment, return to work and all-cause drop outs.Results: We will conduct frequentist random-effects network meta-analysis to assess relative effects of competing interventions.We will use a priori hypotheses to explore heterogeneity between studies, and assess the certainty of evidence using the GRADE approach.Conclusion: This network meta-analysis will determine the comparative effectiveness of therapeutic options for PTSD on both symptom reduction and functional recovery.Our results will be helpful to clinicians and patients with PTSD, by providing a high-quality evidence synthesis to guide shared-care decision making.Abbreviations: CAPS = clinician-administered PTSD scale, CI = confidence interval, DSM = diagnostic and statistical manual of mental disorders, GRADE = grading of recommendations assessment, development, and evaluation, MCS = mental component summary, NMA = network meta-analysis, PCS = physical component summary, PTSD = post-traumatic stress disorder, SUCRA = surface under the cumulative ranking curve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 | 0.100 |
| Meta-epidemiology (narrow) | 0.009 | 0.006 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.048 | 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; 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".