FEASIBILITY OF A ROAD TRAFFIC INJURY SURVEILLANCE INTEGRATING POLICE AND HEALTH INSURANCE DATASETS IN THE DOMINICAN REPUBLIC
Bibliographic record
Abstract
Background Road traffic injuries (RTIs) are underreported in low- and middle-income countries (LMICs). Previous work from some LMICs estimated the RTI burden by manually matching records in capture–recapture methods. Aims/Objectives/Purpose This study assessed the feasibility of semi-automated matching of RTI cases in different datasets in a LMIC. Methods Study population consisted of RTI reported cases in the Dominican Republic in 2010. After removing duplicates and correcting fatality reporting using forensic data, the police and health insurance RTI records were matched if they had same province, date of crash, and gender of RTI cases and similar age (within 5 years). A multinomial logistic regression model assessed likelihood of being unmatched in either dataset. Results/Outcomes Duplicates represented 21.1% of 6396 police and 16.2% of 6178 insurance records. Health insurance recorded 43 of 417 deaths as only injured. Capture–recapture estimated that both datasets recorded one of five cases. Characteristics associated with being unmatched in police dataset were female gender (OR=2.5), age≥16 years (OR=1.7), crash in the regions of Cibao-Northeast (OR=4.1) and Valdesia (OR=6.4), Tuesday to Saturday (1.5≤OR≤2.9), October to December (1.6≤OR≤4.5), and occupant of four-wheeled (OR=5.4) or trucks (OR=5.3). Significance/Contribution to the Field Semi-automated matching is feasible to reliably ascertain RTI burden in the Dominican Republic, but could be improved by standardised coding of police and health insurance reporting.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".