A Procedure and Complication-Specific Assessment of Smoking in Aesthetic Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The popularity of aesthetic surgery is on the rise, as is patients’ expectations towards excellent surgical results. In order to meet these expectations, risk factors that hinder desired outcomes, such as smoking, need to be identified and addressed. To that end, the present study summarizes an updated systematic review focused on the effects of smoking on cosmetic surgical procedures and outcomes. Methods: A systematic review of studies comparing aesthetic surgical outcomes by procedure, between tobacco smokers and non-smokers was carried out, querying PubMed, Embase and the Cochrane databases. Data regarding surgical outcomes were extracted and meta-analyzed by a random effects model in conjunction with the Mantel-Haenszel statistical method. Results: Eighty-two studies were included in the final synthesis. Abdominoplasty/panniculectomy (n = 19 cohorts) and breast reduction (n = 27 cohorts) were the most common types of procedures included in this review. Other than mastopexy and rhinoplasty, smoking conferred a statistically significant increased risk of overall complications for all studied aesthetic procedures. Conclusions: The data demonstrates that smoking is a clear risk factor for the vast majority of aesthetic plastic surgeries studied. Although our meta-analysis suggests that smoking is not a risk factor for complications in mastopexies and rhinoplasties, these two specific analyses may have been biased, and should therefore be re-evaluated with future additional evidence. The results of this systematic review confirm the importance of smoking cessation and education relative to the outcomes of common cosmetic surgical procedures.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.048 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".