Neurotrauma in the Syrian War: Analysis of 41,143 cases from July 2013-July 2015
Bibliographic record
Abstract
Abstract Introduction: Despite nearly a decade of conflict, little is known about trauma and injuries resulting from the Syrian war.Methods: Secondary analysis was conducted of an administrative dataset of patient presentations to a network of 95 war-affected hospitals in Syria from July 2013 - July 2015. Logistic regression was performed to identify factors associated with mortality of neurotrauma patients. Results: Of 193,618 overall trauma presentations, 41,143 were for neurotrauma (37,410 head trauma, 1,407 spinal trauma and 3,133 peripheral nervous system). There were 31,359 males (76.2%) and 9,784 females (23.8%). Males aged 19-30 years (10,113; 24.6%) were the largest single demographic group. Presumed non-combatants including females, elders and children under 13 years (16,214; 39.4%) were the largest group of patients overall. There were 16,881 (41.0%) presentations with blunt injuries (blunt/crush injuries) and 21,307 (51.8%) patients with penetrating injuries (shrapnel, cut, gunshot). A total of 36,589 patients (89.6%) were treated and discharged from the hospital, 2,100 (5.1%) were transferred to another facility, 2,050 patients (5.0%) died in-hospital, 26 remained in the hospital (0.1%), 108 (0.3%) had unknown disposition. The median length of hospital stay was 1 day. There were 4,034 (9.7%) neurosurgical procedures documented. Patients with combined neurotrauma and general trauma suffered 30 times higher mortality than neurotrauma alone (aOR: 30.4; 95%CI: 20.8-44.2, p<0.0001).Conclusion: The Syrian War resulted in large volumes of neurotrauma patients. Presumed non-combatants comprised 39.4% of patients who survived to treatment at a facility. Further study is needed on long-term needs of neurotrauma victims of the Syrian war.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".