Civilian Gunshot Wounds to the Head: Prognostic Factors Affecting Mortality: Meta-Analysis of 1774 Patients
Bibliographic record
Abstract
Civilian gunshot wounds to the head (cGSWH) are devastating, but there is no consensus regarding prognosis and management. Therefore, we conducted a meta-analysis to identify prognostic factors associated with mortality. PubMed, EMBASE, Scopus, Web of Science, and Cochrane Library were queried for retrospective cohort studies of isolated cGSWH reporting mortality prognostic factors. Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) guidelines were followed. Study quality was assessed using the Newcastle-Ottawa scale. Primary outcome was mortality. Pooled estimates of odds ratios (ORs) and 95% confidence intervals (CIs) were derived using random-effects models. Seventeen (17) observational studies (1774 patients) were identified and included. Factors associated with mortality were: age >40 years (OR, 3.44; 95% CI [1.71-6.91]), suicide attempt (5.78; [3.07-10.87]), Glasgow Coma Scale (GCS) 3-8 compared with 9-15 (38.02; [21.98-65.77]), GCS 3-5 versus 6-8 (15.38; [6.72-35.23], bilateral fixed and dilated pupils versus normal (67.12; [16.67-270.22]), and versus unilateral fixed and dilated pupil (25.35; [5.82-110.41]), dural penetration (29.07; [4.30-196.53]) and bihemispheric (4.23; [2.32-7.68]), and multi-lobar injuries (6.53; [1.99-21.42]). Selection for operative management, according to expert neurosurgical opinion, was protective (0.06; [0.01-0.22]). This is the first meta-analysis on cGSWH mortality prognostic factors. Increasing age, suicide attempt, lower GCS, bilateral mydriasis, dural penetration, and bihemispheric and multi-lobar injury are associated with increased mortality. This study can serve as a guide to clinicians and will provide directions for future research to develop evidence-based management algorithms.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.045 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".