Injury registration in a developing country. A study based on patients\' records from four hospitals in Dar es Salaam, Tanzania
Bibliographic record
Abstract
BACKGROUND: A recent study conducted in some parts of Tanzania has revealed that injuries rank as the third major leading cause of death among the adult population only after tuberculosis and HIV/AIDS. Critical to any injury prevention activities is a reliable surveillance system. Such a system may for instance be based on hospital registration of injuries. OBJECTIVES: The aim of this study was to evaluate available hospital records for the purpose of describing the epidemiology of injuries among inpatients in four hospitals in Dar es Salaam, Tanzania. METHODS: The study utilized patients' medical records for the year 1998. The final sample included 1098 cases from four hospitals. Data handling and analysis was performed using statistical software SPSS for windows version 10.0. Cross tabulations with Chi-square testing for independence, t-test for difference between means (independent groups) and one way analysis of variance was used. RESULTS: The age group 21 to 30 years formed the largest proportion of injury-related admissions. The male to female ratio was 2.3 to 1. The largest categories of injuries were road traffic injuries (43.7%), violence and assaults (23.5%), and falls (13.8%). Burns accounted for 6.5% of the cases. The following variables were routinely recorded in case notes: gender (100%), nature of injury/principal diagnosis (99.6%), body part injured (99.4%), and age (96.4%). CONCLUSIONS: There is a need for improving the way injuries are recorded in hospitals. Hospitals' records could provide a useful tool for monitoring injury preventive activities in developing countries like Tanzania.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".