A Systematic Review and Meta-analysis on PTSD Following TBI Among Military/Veteran and Civilian Populations
Bibliographic record
Abstract
OBJECTIVES: To determine whether persons with traumatic brain injury (TBI) are at greater risk of developing posttraumatic stress disorder (PTSD) than other trauma-exposed populations without TBI, and whether this risk is even greater in military/veteran settings than in civilian settings. DESIGN: A systematic review and meta-analysis was conducted in 7 databases. Reference lists from the 33 identified studies and other relevant reviews were also searched. RESULTS: The pooled PTSD proportion reached 27% (95% confidence interval = 21.8-33.1) in groups with TBI, which was 2.68 times greater than the observed 11% (95% confidence interval = 8.0-15.0) in groups without TBI. PTSD after TBI was more frequently observed in military samples than in civilians (37% vs 16%). Military and civilian samples were respectively 4.18 and 1.26 times more inclined to have a diagnosis of PTSD after TBI than when there was no TBI. The proportion of PTSD after TBI was concurrently attributable to the methods of the included studies (objectives focused on PTSD diagnosis, type of comparison group) and to characteristics specific to the military setting (country, sex, blast injuries). CONCLUSIONS: TBI diagnosis and military setting represent greater risks for PTSD. The dual diagnosis of TBI and PTSD requires interdisciplinary collaboration, as physical and psychological traumas are closely intertwined.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".