Collecting injury surveillance data in low- and middle-income countries: The Cape Town Trauma Registry pilot
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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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".