Impact of diabetes on crash risks of truck-permit holders and commercial drivers.
Bibliographic record
Abstract
OBJECTIVE: The U.S. and some Canadian government agencies have waived commercial license restrictions for some insulin-using diabetic drivers. However, the U.S. Federal Highway Administration is no longer giving waivers. Scientific evidence to support such regulations has been sparse. This article presents detailed analyses of crash risks for users and nonusers of insulin among diabetic truck-permit holders in Québec, Canada. RESEARCH DESIGN AND METHODS: Diabetic truck-permit holders were group-matched by age to a random sample of healthy permit holders. Data on permits, medical conditions, and crashes involving 13,453 permit holder-years in 1987-1990 were extracted from the files of the public insurer for automobile injuries in Québec. Additional health status data were obtained from the provincial public health insurer. A telephone survey was conducted to collect data on driving patterns and exposure. Risk ratios were estimated using negative binomial regression models. RESULTS: Risk ratios for crashes vary by category of diabetes. Permit holders for single-unit trucks (STs) who are diabetic without complications and not using insulin have an increased crash risk of 1.68 when compared with healthy permit holders of the same permit class. When controlling for risk exposure, commercial drivers with an ST permit and the same diabetic condition have an increased risk of 1.76. Insulin use is not associated with higher crash risk. CONCLUSIONS: The increased crash risk for the group with uncomplicated diabetes not using insulin is a new finding. The lack of consistent increases in crash risks among diabetic commercial drivers with complications or who use insulin may be a "healthy worker effect" masking the real risk, because these licensees have a lower participation rate as professional drivers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".