Risk Factors for Surgical-Site Infection Following Primary Total Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors associated with the development of surgical-site infection (SSI) following total knee arthroplasty (TKA). DESIGN: A case-control study. SETTING: A 1,100-bed, university-affiliated, tertiary-care teaching hospital. METHODS: Case-patients with SSI occurring up to 1 year following primary TKA performed between January 1999 and December 2001 were identified prospectively by infection control practitioners using National Nosocomial Infections Surveillance (NNIS) System methods. Three control-patients were selected for each case-patient, matched by date of surgery. Stepwise logistic regression analysis was used to determine the relation of potential risk factors to the development of infection. RESULTS: Twenty-two patients with infections (6 superficial and 16 deep) were identified. Infection rates per year were 0.95%, 1.07%, and 1.19% in 1999, 2000, and 2001, respectively. Logistic regression analysis identified two variables independently associated with the development of infection: the use of closed suction drainage (odds ratio [OR], 7.0; 95% confidence interval [CI95], 2.1-25.0; P = .0015) and increased international normalized ratio (INR) (OR, 2.4; CI95, 1.1-5.7; P = .035). Factors not statistically associated with the development of infection included age, NNIS System risk index score, presence of various comorbidities, surgeon, duration of procedure or tourniquet time, type of bone cement or prosthesis used, or receipt of blood product transfusions. CONCLUSIONS: The use of closed suction drainage and a high postoperative INR were associated with the development of SSI following TKA. Avoiding the use of surgical drains and careful monitoring of anticoagulant prophylaxis in patients undergoing TKA should reduce the risk of infection.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| 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".