Smoking cessation outcomes of referral to a specialist hospital outpatient clinic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hospital visits are an opportunity to engage smokers in tobacco treatment. However, little is known about engagement in follow-up referrals. The purpose of this study is to report the rates of program engagement and smoking cessation outcomes of patients referred to a specialist outpatient tobacco treatment program after a hospital visit or other referral. METHODS: A retrospective chart review was used to examine the outcomes of 486 participants referred to a hospital-based smoking cessation clinic provided by tobacco treatment specialists. Referral sources, demographics and smoking, medical, psychiatric, and substance use history were obtained. The main outcomes of interest were engagement in the program and 7-day point-prevalence of smoking abstinence. RESULTS: Sixty-eight percent of participants who were referred to the program were considered "engaged," of which 70% were from hospitals, 4% from community programs, 11% were from general practitioners, and 16% were self-referrals. Thirty-percent (98/331) of engagers were abstinent by time of chart review (30% from the hospital, 8% from community programs, 19% from general practitioners, and 39% of self-referrals). Having quit for 1 month or longer at the past quit attempt, greater confidence in quitting smoking, lower expired carbon monoxide levels at baseline, and greater duration in the program were significant predictors of successful smoking cessation. DISCUSSION AND CONCLUSION: Providing tobacco treatment follow-up and referral for smokers after a hospital visit is important to enhance smoking cessation efforts. SCIENTIFIC SIGNIFICANCE: Referral to evidence-based tobacco treatment after hospital visits is effective. Models of tobacco treatment based on sources of referral should further be explored.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".