A community-based pharmacist-led smoking cessationprogram, before elective total joint replacement surgery,markedly enhances smoking cessation rates
Bibliographic record
Abstract
INTRODUCTION: We compared smoking cessation outcomes between those who used a pharmacist-led community-based smoking cessation intervention and those who did not, prior to total joint replacement (TJR) surgery. Also, we examined intervention characteristics (e.g. number/duration of sessions attended, recommended therapy) and smoking cessation outcomes. METHODS: This prospective evaluation was nested within a comparative study from a centralized clinic that prepares over 3000 patients annually for TJR and focused on participants referred to the community-based smoking cessation program preoperatively. Pharmacists offered an individualized evidence-based intervention and collected visit, duration and intervention data. Smoking cessation, the primary outcome, was ascertained independently of participating pharmacists at 6 weeks post-operative using exhaled CO monitoring and at 6 months post-recruitment via telephone interview. RESULTS: Of 286 eligible candidates, 104 agreed to participate, with one subsequently withdrawing (n=103). At 6 weeks post-operatively, 66/103 (64%) participants returned for study re-assessment while 63/103 (61%) participants completed the post-recruitment interview at 6 months; non-respondents to study follow-up were considered smokers. Of 103 participants, 58 (56%) consulted with a pharmacist; those who did not consult a pharmacist (n=45) were slightly younger (p=0.02) with significantly higher CO level (p=0.02) on study entry. Validated 7-day point prevalence abstinence (PPA) at 6 weeks post-operative was 11/58 (19%) in pharmacist-compliant participants compared to 2/45 (4%) in non-compliant participants (p=0.04). At 6 months post-recruitment, 19/58 (33%) pharmacistcompliant participants self-reported a 7-day PPA compared to 2/45 (4%) by non-compliant participants (p<0.001). For pharmacist-compliant participants, 33/58 (54%) saw the pharmacist 4 times; the mean overall pharmacist time was 71.8±24.4 minutes/patient with 26/58 (45%) and 19/58 (33%) prescribed nicotine replacement therapy and varenicline, respectively, and 13/58 (22%) not using medication; post hoc analysis suggested varenicline was marginally more effective for smoking cessation than no medication (p=0.04). CONCLUSIONS: Community-based pharmacist-led smoking cessation programs are an effective addition to usual preoperative care for smokers awaiting elective TJR. Using existing community resources led to higher smoking cessation rates in smokers waiting for TJR relative to those not using these resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".