Smokers have increased risk of soft-tissue complications following primary elective TKA
Bibliographic record
Abstract
INTRODUCTION: Smoking has been associated with numerous adverse outcomes following surgical procedures. The purpose of this study was to investigate, whether smoking status at time of surgery influences the outcome of primary TKA. MATERIALS AND METHODS: Six hundred and eighty-one patients who underwent primary TKA between 2003 and 2006 were included in the study. Smoking status was defined as current, former, and never smoker. Complications leading to revisions were assessed until 17 years of follow-up. Functional outcome was evaluated using clinical scores: Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Visual Analogue Scale (VAS) for pain, Short Form-12 Physical and Mental Component Summaries (SF-12PCS/MCS), and Knee Society Function and Knee Score (KSFS and KSKS). RESULTS: At a mean follow-up of 95 months (± 47 months), 124 complications led to revision surgery. Soft-tissue complications (OR, 2.35 [95% CI 1.08-5.11]; p = 0.032), hematoma formation (OR, 5.37 [95% CI 1.01-28.49]; p = 0.048), and restricted movement (OR, 3.51 [95% CI 1.25-9.84]; p = 0.017) were more likely to occur in current smokers than never smokers. Current smokers were more likely to score higher at KSFS (p < 0.001) and SF-12PCS (p = 0.0197) compared to never smokers. For overall revision, differences were noted. CONCLUSION: Current smoking increases risk of soft-tissue complications and revision after primary TKA, especially due to hematoma and restricted movement. Smoking cessation programs could reduce the risk of revision surgery.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".