Adverse effect of smoking on surgical site infection following ankle and calcaneal fracture fixation: a meta-analysis
Bibliographic record
Abstract
Purpose: Studies have reported conflicting findings on the relationship between smoking and surgical site infection (SSI) post fixation for ankle and calcaneal fractures. This meta-analysis explored the effect of smoking on SSI incidence following open reduction and internal fixation (ORIF) of these fractures. Methods: Full-text studies on smoking's influence on post-ORIF SSI rates for closed ankle and calcaneal fractures were sourced from the PubMed, Embase, and Cochrane databases, with no consideration given to language or publication date. Study quality was appraised using the Newcastle-Ottawa Scale. Odds ratios (OR) and the corresponding 95% CIs were determined using random-effects models. This meta-analysis adhered to the PRISMA guidelines and was registered with PROSPERO (CRD42023429372). Results: The analysis incorporated data from 16 cohort and case-control studies, totaling 41 944 subjects, 9984 of whom were smokers, with 956 SSI cases. Results indicated smokers faced a higher SSI risk (OR: 1.62; 95% CI: 1.32-1.97, P < 0.0001) post ORIF, with low heterogeneity (I 2 = 26%). Smoking was identified as a significant deep SSI risk factor (OR: 2.09; 95% CI: 1.42-3.09; P = 0.0002; I 2 = 31%). However, the subgroup analysis revealed no association between smoking and superficial SSI (OR: 1.05; 95% CI: 0.82-1.33; P = 0.70; I 2 = 0%). Conclusion: Smoking is associated with increased SSI risk after ORIF for closed ankle and calcaneus fractures. Although no clear link was found between superficial SSI and smoking, the data underscore the negative influence of smoking on deep SSI incidence.
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.
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.061 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".