The Impact of Smoking on Complications After Operatively Treated Ankle Fractures—A Follow-Up Study of 906 Patients
Bibliographic record
Abstract
OBJECTIVES: This study on patients with operatively treated ankle fractures aimed to investigate the impact of smoking on postoperative complications and especially deep wound infections. DESIGN: Cohort study with prospective follow-up. SETTING: University-associated teaching hospital with advanced trauma care. PATIENTS: A consecutive series of patients (n = 906) operatively treated for an acute ankle fracture during a 3-year period was identified. For the analysis, the patients were categorized as nonsmokers (n = 721) and smokers (n = 185). Data were collected from the department database and completed with a review of the patients' medical charts. MAIN OUTCOME MEASURES: Postoperative complications. RESULTS: Follow-up data at 6 weeks were available for 98.2% of the patients. Postoperative complications of any kind (30.1% versus 20.3%, P = 0.005) as well as deep wound infections (4.9% versus 0.8%, P < 0.001) were more common among smokers than nonsmokers. Multivariable analyses showed that smokers had six times higher odds of developing a deep infection compared with nonsmokers. A more complicated fracture, associated diabetes mellitus, and unsatisfactory operative fracture reduction also enhanced the risk of postoperative complications. CONCLUSIONS: We conclude that cigarette smoking increases the risk of postoperative complications in patients operatively treated for an ankle fracture. Smoking is a considerable risk factor. Therefore, physicians, nurses, and other healthcare professionals should strive to support patients to stop smoking while still under acute treatment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".