Bicyclist deaths and striking vehicles in the USA
Bibliographic record
Abstract
OBJECTIVES: Bicycling is a popular means of transportation that is sometimes associated with injury from collisions. The authors analysed national data for the USA to evaluate bicyclist deaths associated with motor vehicle impacts. METHODS: The authors conducted a population-based case-control analysis of road deaths reported by the National Highway Traffic Safety Administration. The authors included bicyclist deaths from 1 January 2008 to 31 December 2008 (cases), along with the non-bicyclist road deaths immediately before and after the bicyclist death in the same state (controls). Analyses also included linkages to auto appraisal websites to estimate type, size and cost of the motor vehicle involved in each death. RESULTS: A total of 711 bicyclist deaths were included, equivalent to a rate of 2 deaths per million population annually. No state had a rate statistically significantly below the national average whereas Florida was a high outlier with three times the national rate (p<0.001). The typical bicyclist who died was a man travelling in the afternoon or evening. The average estimated resale value of the involved motor vehicle was about one-third higher for bicyclist deaths than control deaths (US$10 603 vs US$8118, p<0.001). Analyses based on median estimated resale value and luxury resale value yielded similar findings. Stratified analyses based on demographics, time and posted speed limits yielded similar discrepancies. Larger motor vehicles were particularly common in bicyclist deaths compared to control deaths, especially freight trucks (11% vs 8%, p=0.008) and large automobiles (43% vs 37%, p=0.004). Conversely, motorcycles were distinctly infrequent in bicyclist deaths compared to control deaths (1% vs 14%, p<0.001). CONCLUSIONS: Large expensive motor vehicles account for a disproportionate share of bicyclist deaths. Bicyclists, motorists, policy-makers and vehicle manufacturers need to consider more imaginative solutions to help prevent future deaths.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".