Fibromyalgia and the Risk of a Subsequent Motor Vehicle Crash
Bibliographic record
Abstract
OBJECTIVE: Motor vehicle crashes are a widespread contributor to mortality and morbidity, sometimes related to medically unfit motorists. We tested whether patients diagnosed with fibromyalgia (FM) have an increased risk of a subsequent serious motor vehicle crash. METHODS: We conducted a population-based self-matched longitudinal cohort analysis to estimate the incidence rate ratio of crashes among patients diagnosed with FM relative to the population norm in Ontario, Canada. We included adults diagnosed from April 1, 2006, to March 31, 2012, excluding individuals younger than 18 years, living outside Ontario, lacking valid identifiers, or having only a single visit for the diagnosis. The primary outcome was an emergency department visit as a driver involved in a motor vehicle crash. RESULTS: The patients (n = 137,631) accounted for 738 crashes during the first year of followup after diagnosis, equal to an incidence rate ratio of 2.44 compared with the population norm (95% CI 2.27-2.63, p < 0.001). The crash rate was more than twice the population norm for those with a new or a persistent diagnosis. The increased risk included patients with diverse characteristics, approached the rate observed among other patients diagnosed with alcoholism, and was mitigated among those who received dedicated FM care or a physician warning for driving safety. CONCLUSION: A diagnosis of FM is associated with an increased risk of a subsequent motor vehicle crash that might justify medical interventions for traffic safety.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".