Epidemiology, clinical characteristics and outcomes of head injured patients in an Ethiopian emergency centre
Bibliographic record
Abstract
INTRODUCTION: Head injury is a leading cause of mortality in Africa. We characterise the epidemiology and outcomes of head injury at an Ethiopian emergency centre. METHODS: We conducted a prospective cohort study of all head injured patients presenting to the Emergency Centre of Tikur Anbessa Specialised Hospital, Addis Ababa. Data was collected via a standardised form from the patient's chart, radiology reports and operative reports. Patients were followed until discharge, facility transfer, death, or 7 days in hospital. Consent was obtained from the patient or substitute decision maker. RESULTS: Among 204 head injured patients enrolled, the majority were <30 years old (51.0%) and male (86.8%). Forty-one percent of injuries occurred from road traffic accidents (RTAs). A significant number of patients had at least one indicator of severe injury on presentation: 51 (25.0%) had a GCS < 9, 53 (26.0%) had multi-system trauma, 95 (46.6%) had ≥1 abnormal vital sign and of the 133 patients with data available, 37 (27.8%) had a Revised Trauma Score (RTS) < 6. Patients injured by RTA were more likely to have indicators of severe injury than other mechanisms, including multi-system trauma (OR 3.2, 95% CI 1.7-6.2, p = 0.00), GCS < 9 (OR 3.7, 95% CI 1.8-7.4, p = 0.00), ≥1 abnormal vital sign (OR 2.5, 95% CI 1.4-4.6, p = 0.00) or an RTS score < 6 (OR 3.6, 95% CI 1.6-8.1, p = 0.00). Overall, 149 (73.0%) patients were discharged from hospital, 34 (16.7%) were transferred to another hospital, and 21 patients died (10.3%). In multivariable analysis, death was significantly associated with age over 60 years (aOR 68.8, 95% CI 2.0-2329.0, p = 0.02), GCS < 9 (aOR 14.8, 95% CI 2.2-99.5, p = 0.01), fixed bilateral pupils (aOR 39.1, 95% CI 4.2-362.8, p < 0.01) and hypoxia (oxygen saturation <90%; aOR 14.2%, 95% CI 2.6-123.9, p = 0.01). CONCLUSION: Head injury represents a significant risk for morbidity and mortality in Ethiopia, of which RTA's increase injury severity. Targeted approaches to improving care of the injured may improve outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".