Stopping smoking prior to elective hip and knee surgery: the impact of visiting a community pharmacist for tobacco management
Bibliographic record
Abstract
Aim To have patients quit smoking prior to elective total joint arthroplasty surgery. Objective Smokers have twice the rate of deep surgical site infections and three times the rate of readmission to hospital after arthroplasty than non-smokers. We assessed the impact of patients obtaining counselling and medical management for their nicotine addiction by visiting a pharmacist in their community on short and long term quit rates. Methods After ethics approval and written informed consent patients attending a pre-surgical assessment clinic were recruited to participate in a pharmacy delivered smoking cessation program. Patients watched a surgery specific video education about the program and smoking status was validated by exhaled CO determination at 30 days after program participation and by self-reported smoking status at 6 months obtained by telephone follow up. Results 103 out of 286 (36%) patients approached agreed to participate in the community pharmacist program. 52% were female with a mean age (SD) of 59 (8.4) years. Mean (SD) Fagerstrom score was 4.0 (2.2) and years smoked 36.9 (11.3). 79% had tried to quit previously. Despite all participants agreeing to see a pharmacist only 58% attended for a visit. The validated 30-day and 6-month continuous abstinence rate was 16% and 18%, respectively for those who saw a pharmacist vs. 2% for non-participants. Conclusion Participation in this study and the pharmacy visit was voluntary. The participation rate was low but for those motivated to participate and to visit the pharmacist and receive treatment and counselling the short and long terms outcomes were significant. Mandating at least a single visit to a pharmacy-driven smoking cessation program for all patients undergoing joint arthroplasty seems worth exploring to enhance smoking cessation prior to surgery. Funding Global Research Award for Tobacco Dependence - (GRAND) - Pfizer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".