Correlates of former smoking in patients with cerebrovascular disease: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To identify multilevel correlates of former smoking in patients with cerebrovascular disease. DESIGN: Secondary data analysis of the Canadian Community Health Survey. METHODS: We used data from the 2007-2008 Canadian Community Health Survey (CCHS). Smoking status (former smoking vs smoker) was described by multilevel correlates of former smoking. A multilevel approach for variable selection for this study was used to understand how multiple levels in society can have an impact on former smoking. The study sample was selected from those respondents of the CCHS that reported they suffered from stroke symptoms. Logistic regression was used to predict former smoking in patients with cerebrovascular disease while controlling for multilevel confounders. Proportions were weighted to reflect the Canadian population. RESULTS: There were 172 355 respondents who reported to suffer from stroke. From this sample, 36.5% were smokers and 63.5% were former smokers. Age groups 55-69 and 70-80 and higher education (secondary education +) were positively related to former smoking. Household and vehicle smoking restrictions significantly predicted former smoking. Counselling advice from a physician and having access to a general practitioner were correlates of former smoking. Finally, the use of buproprion was positively related to former smoking. CONCLUSIONS: There are multilevel correlates of former smoking in smokers with reported stroke symptoms. These correlates include older age groups, higher education, household and vehicle smoking restrictions, pharmacotherapy use (bupropion), access to a general practitioner and counselling advice from a physician.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".