P2-329 What medical conditions and medications used to treat the medical conditions increase the risk of subsequent motor vehicle injuries: results of the Canadian National Population Health Survey
Bibliographic record
Abstract
Introduction The last 2 decades have seen an increased interest in the relationships between medical conditions and medication use and motor vehicle injuries (MVIs). The objective of this study is to examine the effects of various medical conditions and medications used to treat the conditions on subsequent MVIs. Method The National Population Health Survey is a large nationally representative sample of Canadians who have been surveyed every 2 years since 1994. Self-reported medical conditions and medication use were examined in relation to MVIs reported in the subsequent wave of the survey. Respondents were queried on whether they had any of the following long-term conditions: asthma, arthritis/rheumatism, back problems, high blood pressure, migraine headaches, pain, diabetes and heart disease; measures of distress and depression were also included. They were also asked whether they had taken medications to treat these conditions. Medical conditions and medications were subjected to regression analyses where medical conditions and medications served as controls for each other. Results The results found that asthma, back problems, migraine and distress showed statistically significant increased risk of subsequent MVIs. Various medications (asthma medication, Demerol, codeine, pain medication and, sleeping medication) were also associated with increased risk of subsequent MVIs. Finally, for some medical conditions, medications have a protective effect while for other conditions, medications have independent effects on the risk of subsequent MVIs. Conclusion This study suggests that the relationship between medical conditions and medications is complex and in need of further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".