Physician Warnings in Psychiatry and the Risk of Road Trauma
Bibliographic record
Abstract
OBJECTIVE: To determine if physician warnings to psychiatric patients alter the subsequent frequency of a motor vehicle crash. A secondary objective was to determine if physician warnings change the subsequent frequency of psychiatric hospitalization. METHODS: Exposure crossover design of 23,145 psychiatric patients diagnosed with ICD-9 schizophrenia (code 295), mood disorder (296), personality disorder (301), or substance use disorder (303, 304) and warned by their physician about driving safety between April 1, 2006, and March 31, 2011. Each patient was followed for 4 years before the warning and 1 year after the warning. Patients living outside the region or lacking a valid health card number were excluded. RESULTS: Patients' motor vehicle crash frequency decreased from 11.78 to 8.17 events per 1,000 patients per year after a physician warning, which corresponded to a relative risk of 0.69 (95% CI, 0.59-0.81; P < .001). Psychiatric hospitalization frequency increased from 147 to 289 events per 1,000 patients per year corresponding to a relative risk of 1.97 (95% CI, 1.91-2.03; P < .001). CONCLUSIONS: Physician warnings are associated with a subsequent decreased frequency of motor vehicle crashes and increased frequency of psychiatric hospitalization. This result suggests that physician warnings are an effective intervention for reducing road trauma but need to be weighed against potential adverse psychiatric health.
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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 | 0.001 | 0.009 |
| 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) | 0.002 | 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".