Perioperative and Long-Term Smoking Behaviors in Cosmetic Surgery Patients
Bibliographic record
Abstract
BACKGROUND: Many plastic surgeons advocate smoking cessation before patients undergo cosmetic surgery with extensive soft-tissue dissection. Smoking cessation rates after cosmetic surgery are unknown. The preoperative consultation may be an opportunity to promote long-term smoking cessation. METHODS: This is a retrospective, cross-sectional cohort study. All patients over an 8-year study period who smoked before their preoperative consultation; who quit 2 weeks before surgery; and who subsequently underwent rhytidectomy, abdominoplasty, or mastopexy were included. Patients were asked to complete a Web-based survey at long-term follow-up. RESULTS: Eighty-five smokers were included, and 47 patients completed the survey, for a response rate of 55.3 percent. Average follow-up was 63.3 months. Five respondents were social smokers and thus excluded. Of the 42 daily smokers, 17 patients (40.5 percent) were no longer smoking cigarettes on a daily basis at long-term follow-up. Of these 17 patients, 10 (23.8 percent) had not smoked since their operation. A total of 24 patients (57.1 percent) had reduced their cigarette consumption by any amount, and 70.8 percent (17 of 24) of these patients agreed that discussing adverse surgical outcomes related to smoking influenced their ability to quit/reduce. Twenty-one of 42 patients (50.0 percent) admitted that they were not compliant with the preoperative smoking cessation instructions. CONCLUSIONS: This is the first report to investigate long-term smoking cessation from a cosmetic surgery practice. The authors have shown a positive association between smoking cessation and cosmetic surgery. Requesting a period of cessation before cosmetic surgery may promote long-lasting smoking 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".