Close to home
Bibliographic record
Abstract
BACKGROUND: Injury surveillance is critical in identifying the need for targeted prevention initiatives. Understanding the geographic distribution of injuries facilitates matching prevention programs with the population most likely to benefit. At the population level, however, the geographic site of injury is rarely known, leading to the use of location of residence as a surrogate. To determine the accuracy of this approach, we evaluated the relationship between the site of injury and of residence over a large geographic area. METHODS: Data were derived from a population-based, prehospital registry of persons meeting triage criteria for major trauma. Patients dying at the scene or transported to the hospital were included. Distance between locations of residence and of injury was calculated using geographic information system network analysis. RESULTS: Among 3,280 patients (2005-2010), 88% were injured within 10 miles of home (median, 0.2 miles). There were significant differences in distance between residence and location of injury based on mechanism of injury, age, and hospital disposition. The large majority of injuries involving children, the elderly, pedestrians, cyclists, falls, and assaults occurred less than 10 miles from the patient's residence. Only 77% of motor vehicle collision occurred within 10 miles of the patient's residence. CONCLUSION: Although the majority of patients are injured less than 10 miles from their residence, the probability of injury occurring "close to home" depends on patient and injury characteristics. LEVEL OF EVIDENCE: Epidemiologic study, level III.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.006 |
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".