Impact of diabetes on crash risks of truck-permit holders and commercial drivers.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it