A Retrospective Review of Pilot Outcomes from an Out-patient Tobacco Treatment Programme Within Cardiology Services
Bibliographic record
Abstract
Introduction: Due to the challenging nature of tobacco addiction, individuals with cardiac conditions often continue to smoke at high rates (up to 62%), even after experiencing life-threatening events. Aims: This study examines pilot outcomes of a longitudinal Smoking Cessation Clinic (SCC) within cardiology services. Methods: This study is based on a retrospective review of the charts of 117 participants of the SCC (between September 2010 and May 2012). The main outcome of interest is self-reported 7-day point-prevalence of smoking abstinence verified by expired CO level. A secondary outcome was smoking reduction, defined as consuming 50% (or lower) than the baseline number cigarettes in the past week. Results: Thirty-five per cent of participants achieved smoking cessation, whereas 42.1% reduced their cigarette consumption. In multivariate regression analyses, salient predictors of smoking cessation included being male and a greater length of visiting the smoking cessation clinic. Conclusions: Providing evidence-based approaches to tobacco treatment within cardiology services is feasible and well received by patients with cardiac and other co-morbidity. The modest outcomes from this pilot study support the need for tobacco treatment in hospital cardiology settings. Such interventions may aid in reducing the disproportionate burden of tobacco-related disease among smokers with medical co-morbidity.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".