Crash risk in morbidly obese drivers before and after bariatric surgery: A population-based cohort study
Bibliographic record
Abstract
Morbid obesity is a major public health problem in high-income, developed and fast developing countries. The potential effects of morbid obesity on road crash risk have rarely been investigated. In this study, we evaluated road crash risks in morbidly obese drivers who underwent bariatric surgery. We conducted a self-matched cohort analysis of morbidly obese adult patients in Ontario (Canada) who underwent bariatric surgery between April 1, 2006 and March 31, 2011. We used a province-wide emergency department database to determine their involvement in a road crash as a driver. We compared crash incidence per 1,000 patient-years in the three-year interval before surgery to three years after surgery. The cohort included a total of 8,815 patients; most (81%) were women. About 4% (n=333) of them were involved in a crash during six years of follow-up. Of them, 175 had 182 crashes before surgery and 165 patients had 174 crashes after surgery. The road crash incidence was similar before and after surgery, i.e., 7 per 1,000 patient-years. This road crash incidence was three times higher than the population rate of 2 per 1,000 patient-years. Crash risks were similar before and after surgery among those who were diagnosed with an obstructive sleep apnea (Incidence rate ratio [IRR]= 0.95, 95% Confidence Interval [CI] = 0.73 - 1.23) and other patients (IRR = 0.96; 95% CI=0.69-1.37). Morbid obesity may be associated with increased crash risk. These findings favor crash risk assessment in morbidly obese drivers.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".