Ability of Surgical Residents to Impact Patient Tobacco Use in the Perioperative Setting
Bibliographic record
Abstract
INTRODUCTION: Tobacco use significantly impacts patient morbidity and mortality, and is particularly important to the surgical community as it has a negative impact on surgical recovery. Despite its relevance, most surgeons do not engage in smoking cessation counseling. METHODS: As part of the UCSF Medical Center quality improvement program, urology residents designed and led an initiative targeting urological patients undergoing surgery at UCSF between August 2013 and July 2014. Our primary objectives were to 1) elicit the smoking status of at least 80% of perioperative patients who stayed at least 1 night in the hospital and 2) obtain an inpatient smoking cessation consultation for patients who were active tobacco users. RESULTS: A total of 934 patients were assessed, of whom 8.5% were current tobacco users, 37% were past users and 55% had never used tobacco. Current smokers were more likely to be younger and female, and less likely to have a cancer diagnosis compared to past or never smokers (all p ≤0.001). The rate of tobacco status assessment improved significantly throughout the quality improvement period, at 59.0% in quarter 1, 85.5% in quarter 2, 92.9% in quarter 3 and 94.4% by quarter 4. Patients who underwent smoking cessation consults were more likely to be prescribed nicotine replacement therapy during their hospital stay and upon discharge home. CONCLUSIONS: Through our quality initiative program we demonstrated a fast, feasible and easily implemented approach to identify smokers, and obtain smoking cessation counseling and treatment. This practical approach represents a significant opportunity to effect change in smoking behavior at a teachable moment.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".