The effectiveness of smoking cessation interventions in smokers with cerebrovascular disease: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The main objective of this study was to determine the effectiveness of smoking cessation interventions (SCIs) for increasing cessation rates in smokers with cerebrovascular disease. DESIGN: Systematic review. Two independent reviewers searched information sources and assessed studies for inclusion/exclusion criteria. ELIGIBILITY CRITERIA FOR INCLUDED STUDIES: Randomised control trials, conducted prior to the 22 May 2012 investigating SCIs in smokers with cerebrovascular disease, were included. No age or ethnicity limitations were applied in order to be as inclusive as possible. METHODS: We followed the PRISMA statement approach to identify relevant randomised control studies. Due to the variability of interventions used in the reported studies, a meta-analysis was not conducted. RESULTS: Of 852 identified articles, 4 articles fit the inclusion criteria describing the outcome in 354 patients. The overall cessation rate with an SCI was 23.9% (42 of 176) while without one was 20.8% (37 of 178). CONCLUSIONS: There are a limited number of reported intervention studies that explore this area of secondary stroke prevention. Furthermore, of those intervention studies that were found, only two implemented evidence-based approaches to smoking cessation. A meta-analysis was not conducted because of the variability of interventions in the reported studies. Larger studies with homogeneous interventions are needed to determine how effective SCIs are in increasing cessation in smokers with established 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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".