Smoking and Cerebrovascular Disease: A Three-phase Research Program
Bibliographic record
Abstract
Purpose: The purpose of this research program was three-fold. First it aimed to determine the effectiveness of smoking cessation interventions in increasing cessation rates in smokers with cerebrovascular disease and whether smoking cessation reduces stroke recurrence. Second it aimed to determine the prognostic influence of smoking and its association with stroke severity, disability, length of stay in hospital and mortality. Third it aimed to identify multi-level correlates of smoking cessation in Canadians who reported stroke symptoms in a large population based survey. Methods: Two systematic reviews and meta-analyses were performed to achieve the first objective. For the second objective, a retrospective cohort study was undertaken using variables from the Registry of the Canadian Stroke Network. Finally, the third objective was achieved by analyzing respondents from the Canadian Community Health Survey. Results: There is a paucity of intervention studies examining the effectiveness of smoking cessation in smokers with cerebrovascular disease. Most intervention studies that were found, failed to employ evidence-based approaches to smoking cessation. No evidence was found in regards to the effect of smoking cessation on stroke recurrence. We found smokers had strokes at a younger age compared to non-smokers. We found that in transient ischemic attacks and intracerbral haemorrhage, smoking was a significant predictor of stroke severity, disability, length of stay in hospital and 1 year mortality. Correlates of smoking cessation among Canadians who have experienced symptoms of a stroke included: higher education and income, implementation of household and vehicle smoking restrictions, access to a general practitioner and the use of smoking cessation pharmacotherapies and counselling support. Co-morbidities such as depression and alcohol consumption reduced the likelihood of successful cessation. Conclusions: This three-phase research program elucidated the gaps in intervention research for this population along with co-morbidities that hinder success in cessation. Smoking negatively impacted outcomes such as disability, hospital length of stay and mortality in patients with transient ischemic attacks and intracerebral haemorrhage strokes. Future interventions should take into account modifiable smoking cessation correlates in order to increase cessation rates in smokers with cerebrovascular disease.
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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 | 0.172 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".