Analysis of trauma patients in a rural hospital in Turkey
Bibliographic record
Abstract
BACKGROUND: There is a grey zone about the epidemiology of trauma in eastern Turkey. The present study was aimed at obtaining data on this subject. METHODS: Trauma patients who applied to the emergency department (ED) between January 2006 and December 2007 were analyzed. RESULTS: There were 6183 patients, of whom 87% were male. The mean age was 26.2 ± 13.6 years. Assault was the most common cause (63.2%). Motor vehicle injury (MVI) and fall were encountered at frequencies of 21.2% and 6.5%, respectively. The most frequently injured body regions were head-neck and extremities. The majority of patients were managed and discharged from the ED (89.8%) with no consultation (81.8%). Interestingly, the discharge rate of assault cases was 98.7%. Patients were hospitalized (4.2%) mostly for MVI (32.6%) and fall (19%); however, hospitalization rates for firearm and piercing/cutting injury (36.1% and 16.7%) were significantly high. Among the transported patients (5.3%), the rates of MVI and fall were high (41.5% and 24.3%, respectively). In groups, for burn and firearm injuries, these were 42.1% and 24.1%, respectively. Forty-eight patients (0.8%) died, mostly from MVI by number, but by self-infliction and firearm by rate (8.3% and 6%). CONCLUSION: Assault cases caused an excessive trauma patient density in the ED, as 98.7% were discharged from the ED. Further studies are needed regarding the high rate of assault cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".