Motorcycle-related trauma in Alberta: a sad and expensive story.
Bibliographic record
Abstract
BACKGROUND: Trauma caused by motorcycle-related injuries is extensive, expensive and increasing. Recent American literature reported that in 2004 the chance of a motorcyclist dying was 34 times greater than that for someone using any other motor vehicle for every mile travelled. In the United Kingdom a motorcyclist is killed or seriously injured every 665,894 km, compared with 18,661,626 km for cars. If this pattern is repeated in Canada, then this information should be in the public domain to support initiatives for injury prevention. METHODS: We gathered and analyzed retrospective population data on the injury patterns of adult motorcyclists and other adult motor vehicle drivers and passengers across Alberta from Apr. 1, 1995, to Mar. 31, 2006. We collected data from 3 Alberta sources: the Alberta Trauma Registry, the Alberta Office of the Chief Medical Examiners and the Government of Alberta Department of Infrastructure and Transportation. We compared the numbers and causes of crashes, injuries and deaths, as well as the acute care costs on the roads, and specifically compared motorcycle-related injuries to all other motor vehicle-related injuries. RESULTS: There were 70,605 registered motorcycles and 2,748,204 other registered motor vehicles in Alberta during the study period. During these 11 years, there were 286 motorcyclists killed and 712 were severely injured, representing a total of 998 injuries and deaths. There was 5386 deaths related to other motor vehicles and 6239 severe injuries, for a total of 11,625 injuries and deaths. This represents a percentage of 1.4% of all registered motorcycles and 0.4% of all other registered motor vehicles (3.5 times more motorcyclist injuries). The impact on the health care system can be measured in several ways. During the period of this study, motorcyclists accounted for 10,760 bed days. Assuming the patient was not admitted to intensive care, each admission cost Can$9200 (average in 2008). CONCLUSION: Analysis of the data shows that motorcyclists are more than 3.5 times more likely to get injured or die than other motor vehicle drivers. All of the injuries in motorcyclists occurred during the summer months, leading to an adjusted risk of almost 8 times compared with that of the motor vehicle driver.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".