Diabetes and Driving Mishaps
Bibliographic record
Abstract
OBJECTIVE: The intensive treatment of diabetes to achieve strict glycemic control is a common clinical goal, but it is associated with an increased incidence of hypoglycemia. Becoming hypoglycemic while driving is a hazardous condition and may lead to a greater incidence of driving mishaps. This study investigated whether diabetes is associated with increased risk of driving mishaps and correlates of such a relationship. RESEARCH DESIGN AND METHODS: During routine visits to diabetes specialty clinics in seven U.S. and four European cities, consecutive adults with type 1 diabetes, type 2 diabetes, and nondiabetic spouse control subjects (n = 341, 332, and 363, respectively) completed an anonymous questionnaire concerning diabetes and driving. RESULTS: Type 1 diabetic drivers reported significantly more crashes, moving violations, episodes of hypoglycemic stupor, required assistance, and mild hypoglycemia while driving as compared with type 2 diabetic drivers or spouse control subjects (P < 0.01-0.001). Type 2 diabetic drivers had driving mishap rates similar to nondiabetic spouses, and the use of insulin or oral agents for treatment had no effect on the occurrence of driving mishaps. Crashes among type 1 diabetic drivers were associated with more frequent episodes of hypoglycemic stupor while driving, less frequent blood glucose monitoring before driving, and the use of insulin injection therapy as compared with pump therapy. One-half of the type 1 diabetic drivers and three-quarters of the type 2 diabetic drivers had never discussed hypoglycemia and driving with their physicians. CONCLUSIONS: Type 1 diabetic drivers are at increased risk for driving mishaps, but type 2 diabetic drivers, even on insulin, appear not to be at a higher risk than nondiabetic individuals. Clinical and treatment factors appear to increase risk, e.g., more frequent hypoglycemia while driving, method of insulin delivery, and infrequent self-testing before driving. Physicians are encouraged to talk to their type 1 diabetic patients about hypoglycemia and driving.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".