Epidemiology of Traumatic Injuries at an Urban Hospital in Port‐au‐Prince, Haiti
Bibliographic record
Abstract
BACKGROUND: Traumatic injuries represent a major burden of disease worldwide. Haiti lacks statistics on the epidemiology of traumatic injuries, as there is no formal injury surveillance program. This study will assess the burden of traumatic injuries in an urban trauma center in the capital city of Port-au-Prince, Haiti. METHODS: A retrospective, cross-sectional chart review study at an urban trauma hospital was carried out for the period December 1, 2015, to January 31, 2016. Data were obtained through the hospital's main patient logbook, medical charts, and trauma registry forms. Data on medical documentation, demographics, and injury characteristics were collected and analyzed using descriptive statistics. RESULTS: A total of 410 patients were evaluated for treatment of traumatic injuries during the 2-month study. The mean age in years was 30, with 66.3% male and 78.4% less than 41 years of age. There were 6.6 injuries per day and no correlation between frequency of injury and day of the week. Road traffic accidents accounted for 43.0% of trauma modes. The mean and median length of stay were 6.6 and 3.0 days. 9.0% of patients suffered severe trauma (ISS ≥ 16). 21.0% of patients with traumatic brain injury suffered severe head injuries. Extremity trauma was the most frequently injured anatomical region (50.0%). 22.7% of patients were admitted, and 15.1% patients underwent at least one surgical procedure. CONCLUSIONS: Road traffic accidents are the primary reason for injury; thus, prevention initiatives and improved trauma care may provide substantial public health benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".