The Effect of Smoking on Clinical Outcome and Complication Rates Following Ilizarov Reconstruction
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of smoking on outcome and complication rates following Ilizarov reconstruction. DESIGN: We performed a retrospective review of 84 adult patients (86 limbs) who underwent Ilizarov reconstruction. There were 39 "limbs" in nonsmokers and 47 "limbs" in active smokers. Complications and an outcome score based on ASAMI (Association for the Study and Application of the Methods of Ilizarov) criteria were recorded for each patient. DATA ANALYSIS AND RESULTS: There were 35 major complications including 15 malunions/nonunions, 7 refractures, 8 persisting infections, and 5 amputations. Results were measured using the ASAMI outcome scale. There were significantly more poor results in the smoking group than in the nonsmoking group (18/47, 38% versus 4/39, 10%; P = 0.003). Seven of eight patients with persisting infection were smokers (P = 0.049). There was a higher incidence of nonunion in the smoking group (P = 0.031). All five amputations were in smokers (P = 0.035). CONCLUSION: Smokers had a higher percentage of poor results (P = 0.01), due primarily to higher complication rates. Smoking is a significant, potentially remediable risk factor for failure following Ilizarov reconstruction, and cessation strategies are of paramount importance prior to initiating 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 | 0.002 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".