Implementation of a systematic tobacco treatment protocol in a surgical outpatient setting: a feasibility study
Bibliographic record
Abstract
Background: Smoking cessation programs started as late as 4 weeks before surgery reduce perioperative morbidity and death, yet outpatient clinic interventions are rarely provided. Our aim was to evaluate the feasibility of implementing a tobacco treatment protocol designed for an outpatient surgical setting. Methods: We completed a pre-post feasibility study of the implementation of a systematic, evidence-based tobacco treatment protocol in an outpatient colorectal surgery clinic. Outcomes included smoking prevalence, pre- and postimplementation smoker identification and intervention rates, recruitment, retention, smoking cessation and provider satisfaction. Results: Preimplementation, 15.5% of 116 surveyed patients were smokers. Fewer than 10% of surveyed patients reported being asked about smoking, and none were offered any cessation intervention. Over a 16-month postimplementation period, 1198 patients were seen on 2103 visits. Of these, 950 (79.3%) patients were asked smoking status on first visit and 1030 (86.0%) were asked on at least 1 visit. Of 169 identified smokers, 99 (58.6%) were referred to follow-up support using an opt-out approach. At 1-, 3- and 6-month follow-up, intention-to-quit rates among 78 enrolled patients were 24.4%, 22.9% and 19.2%, respectively. Postimplementation staff surveys reported that the protocol was easy to use, that staff would use it again and that it had positive patient responses. Conclusion: Implementation of our smoking cessation protocol in an outpatient surgical clinic was found to be feasible and used minimal clinic resources. This protocol could lead to increases in identification and documentation of smoking status, delivery of smoking cessation interventions and rates of smoking reduction and cessation.
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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 | 0.078 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".