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Record W2009910575 · doi:10.1080/15389588.2012.654411

Medical Conditions, Medication Use, and Their Relationship With Subsequent Motor Vehicle Injuries: Examination of the Canadian National Population Health Survey

2012· article· en· W2009910575 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTraffic Injury Prevention · 2012
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineOdds ratioPethidineAsthmaPopulationConfidence intervalDiabetes mellitusRheumatismPhysical therapyInternal medicineAnesthesiaAnalgesicEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE: To examine the effects of various medical conditions and medications on subsequent motor vehicle injuries (MVIs). METHOD: The National Population Health Survey, a large, nationally representative, longitudinal study of Canadians, included self-reported medical conditions of asthma, arthritis/rheumatism, back problems excluding arthritis, high blood pressure, migraine headaches, diabetes, heart disease and distress, and medication use during the past month for asthma, high blood pressure, diabetes, heart, codeine/pethidine (Demerol)/morphine, other pain relievers, antidepressants, tranquilizers, and sleeping medication. Path analyses were used to examine the odds of subsequent MVI for different medical conditions and medication use reported prior to the MVI (in the previous wave of the survey) while controlling for age and sex. RESULTS: Increased odds of subsequent MVIs were found for asthma (odds ratio [OR]: 1.864, 95% confidence interval [CI]: 1.281, 2.713), arthritis/rheumatism (OR: 1.659, 95% CI: 1.163, 2.365), back problems (OR: 2.169, 95% CI: 1.624, 2.895), and migraines (OR: 1.631, 95% CI: 1.125, 2.364) but not for high blood pressure (OR: 1.435, 95% CI: 0.944, 2.181), diabetes (OR: 1.479, 95% CI: 0.743, 2.944), heart disease (OR: 2.627, 95% CI: 0.941, 7.334) or distress (OR: 1.153, 95% CI: 0.840, 1.581). Except for migraine with codeine/pethidine/morphine, this effect persisted regardless of whether medication was used to treat the condition. Respondents who reported using certain medications, namely, codeine/pethidine/morphine (OR: 2.215, 95% CI: 1.274, 3.850), other pain medication (OR: 1.630, 95% CI: 1.242, 2.139), antidepressants (OR: 2.664. 95% CI: 1.602, 4.429), and sleeping medication (OR: 2.059, 95% CI: 1.161, 3.651), had increased odds of subsequent MVI, independent of related medical condition, whereas tranquillizers showed no increased odds of subsequent MVIs. CONCLUSIONS: This study suggests that the relationship between medical conditions, medications, and MVIs is complex but consistent with other studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.389
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it