An increased risk of road traffic accidents after prescriptions of lithium or valproate?
Bibliographic record
Abstract
OBJECTIVES: Studies have shown that lithium may cause psychomotor and cognitive impairment and impose an increased risk of traffic accidents. The antiepileptic drug valproate is also used as a mood stabilizer, but the impact on traffic safety has not been studied. The objective of the present study was to examine whether the use of lithium or valproate increased the risk of being involved in traffic accidents. METHODS: Between April 2004 and September 2006, information on prescriptions, road accidents and emigrations/deaths was obtained from three Norwegian population-based registries. Data on people between the ages 18-70 (3.1 million) were linked. Exposure consisted of receiving prescriptions for either lithium or valproate. Standardized incidence ratios (SIRs) were calculated by comparing the incidence of motor vehicle accidents during time exposed with the incidence over the time not exposed. Lithium was studied separately from valproate. RESULTS: During the study period, 20,494 road accidents occurred including 36 while exposed to lithium and 31 while exposed to valproate. The overall accident risk was neither increased after having received prescriptions for lithium (SIR 1.3; 95%CI: 0.9-1.8), nor after having received a prescription for valproate (SIR 0.9; 0.6-1.3). The exception was a three-fold increase in risk for younger female drivers exposed to lithium. CONCLUSIONS: We found no increase in the traffic accident risk after being exposed to lithium or valproate, except for young female drivers on lithium. This may be because these drugs carry no increased risk or because patients exposed to these drugs refrain from driving.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".