The association between diabetes and safe driving: A systematic search and review of the literature and cross‐reference with the current guidelines
Bibliographic record
Abstract
AIMS: We conducted this review to characterize the quality of evidence about associations between diabetes and safe driving and to evaluate how these findings are reflected within current guidelines available to support clinicians and their patients with diabetes. METHODS: The first stage entailed a systematic search and review of the literature. Evidence surrounding harms associated with diabetes and driving was identified, screened, extracted and appraised for quality utilizing the Newcastle Ottawa Scales (NOS). Next, relevant guidelines regarding driving and diabetes were sourced and summarized. Finally, the identified guidelines were cross-referenced with the results of the systematic search and review. RESULTS: The systematic search yielded 12,461 unique citations; 52 met the criteria for appraisal. Fourteen studies were rated as 'high', two as 'medium' and 36 as 'low'. Studies with ratings of 'high' or 'medium' were extracted, revealing a body of inconsistent methods and findings. These results, cross-referenced with the guidelines, suggest a lack of agreement and a limited evidence base to justify recommendations. CONCLUSIONS: The results presented emphasize the need for a better understanding of the impacts of diabetes on safe driving to inform evidence-based guidelines.
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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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".