Collecting injury surveillance data in low- and middle-income countries: The Cape Town Trauma Registry pilot
Why this work is in the frame
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Bibliographic record
Abstract
Injury is a major public health issue, responsible for 5 million deaths each year, equivalent to the total mortality caused by HIV, malaria and tuberculosis combined. The World Health Organisation estimates that of the total worldwide deaths due to injury, more than 90% occur in low- and middle-income countries (LMIC). Despite the burden of injury sustained by LMIC, there are few continuing injury surveillance systems for collection and analysis of injury data. We describe a hospital-based trauma surveillance instrument for collection of a minimum data-set for calculating common injury scoring metrics including the Abbreviated Injury Scale and the Injury Severity Score. The Cape Town Trauma Registry (CTTR) is designed for injury surveillance in low-resource settings. A pilot at Groote Schuur Hospital in Cape Town was conducted for one month to demonstrate the feasibility of systematic data collection and analysis, and to explore challenges of implementing a trauma registry in a LMIC. Key characteristics of the CTTR include: ability to calculate injury severity, key minimal data elements, expansion to include quality indicators and minimal drain on human resources based on few fields. The CTTR provides a strategy to describe the distribution and consequences of injury in a high trauma volume, low-resource environment.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 it