The Impact of Smoking on Complications After Operatively Treated Ankle Fractures—A Follow-Up Study of 906 Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it