Posttraumatic stress disorder prevalence in medical populations: A systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: PTSD is increasingly recognized following medical traumas although is highly heterogeneous. It is difficult to judge which medical contexts have the most traumatic potential and where to concentrate further research and clinical attention for prevention, early detection and treatment. The objective of this study was to compare PTSD prevalence in different medical populations. METHODS: A systematic review of the literature on PTSD following medical traumas was conducted as well as a meta-analysis with final pooled result and 95% confidence intervals presented. A meta-regression was used to investigate the impact of potential effect modifiers (PTSD severity, age, sex, timeline) on study effect size between prevalence studies. RESULTS: From 3278 abstracts, the authors extracted 292 studies reporting prevalence. Using clinician-administered reports, the highest 24 month or longer PTSD prevalence was found for intraoperative awareness (18.5% [95% CI=5.1%-36.6%]) and the lowest was found for epilepsy (4.5% [95% CI=0.2%-12.6%]). In the overall effect of the meta-regression, only medical events or procedures emerged as significant (p = 0.006) CONCLUSION: This review provides clinicians with greater awareness of medical contexts most associated with PTSD, which may assist them in the decision to engage in more frequent, earlier screening and referral to mental health services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it