Trauma in the Neighborhood: A Geospatial Analysis and Assessment of Social Determinants of Major Injury in North America
Bibliographic record
Abstract
OBJECTIVES: We sought to identify and characterize areas with high rates of major trauma events in 9 diverse cities and counties in the United States and Canada. METHODS: We analyzed a prospective, population-based cohort of injured individuals evaluated by 163 emergency medical service agencies transporting patients to 177 hospitals across the study sites between December 2005 and April 2007. Locations of injuries were geocoded, aggregated by census tract, assessed for geospatial clustering, and matched to sociodemographic measures. Negative binomial models were used to evaluate population measures. RESULTS: Emergency personnel evaluated 8786 major trauma patients, and data on 7326 of these patients were available for analysis. We identified 529 (13.7%) census tracts with a higher than expected incidence of major trauma events. In multivariable models, trauma events were associated with higher unemployment rates, larger percentages of non-White residents, smaller percentages of foreign-born residents, lower educational levels, smaller household sizes, younger age, and lower income levels. CONCLUSIONS: Major trauma events tend to cluster in census tracts with distinct population characteristics, suggesting that social and contextual factors may play a role in the occurrence of significant injury events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".