Analysis of 193,618 trauma patient presentations in war-affected Syria from July 2013 to July 2015
Bibliographic record
Abstract
Abstract Introduction Since 2011, the Syrian war has produced a mounting toll in terms of deaths and displaced persons. We present an analysis of demographic and temporal patterns of trauma patient presentations to Syrian hospitals in non-governmental, non-Islamic State (NGNI) regions from 2013 – 2015. Methods We analyzed an administrative dataset of patient presentations to 95 NGNI Syrian hospitals in regions outside of Syrian government control from July 2013 – July 2015. Descriptive analysis of this secondary data is reported and logistic regression was performed to assess for factors associated with inpatient mortality. Results 193,618 trauma patients presented to 95 NGNI hospitals from July 2013 – July 2015 (154,225 male, 79.7%; 39,393 female, 20.4%). Age information was complete for 160,237 encounters (82.8%): 0-2y: 8,257 (4.3%), 3-12y: 24,199 (12.5%), 13-18y: 22,482 (11.6%), 19-60y: 100,553 (51.9%), and elders over 60 years: 4,746 (2.5%). 59,387 patients were admitted (Ward 57,625; ICU 1,762) for an average length of stay of 3.80 days. There were 2,694 inpatient deaths (4.5% of admitted) and 4,758 patients (8.0%) required transfer to another facility for definitive care. Shrapnel (81,946; 42.3%) and blunt/crush injuries (71,477; 36.9%) were dominant injury mechanisms with an increasing proportion of these injuries over time. Inpatient mortality was most associated with extremes of age (age less than 2 aOR 2.92; age greater than 60 aOR 2.48), penetrating chest trauma (gunshot-chest aOR 6.03) and neurotrauma (blast-head aOR 13.42; blast-spine aOR 11.31; gunshot-head aOR 10.07; shrapnel-head aOR 6.34). Civilians presentations increased from 20% at start of data collection to a peak of 50% in June 2015. Conclusion The Syrian war has resulted in large volumes of trauma patients and significant mortality at NGNI Syrian hospitals. Mortality was most associated with neurotrauma and penetrating chest trauma. There was an increasing trend over time towards blunt/crush and shrapnel injuries consistent with the transition to the widespread use of aerial bombardment with resultant explosions and building collapse. Civilians including children and the elderly represent high proportions of the injured in NGNI Syrian hospitals. Additional work is needed to improve documentation of clinical service and to assess outcomes of care to improve quality of services provided to Syrian war trauma patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".