Annual incidence and assessment of risk factors for early-onset deep surgical site infection following primary total knee arthroplasty in osteoarthritis
Bibliographic record
Abstract
Aims: This study aims to determine difference in annual rate of early-onset (≤ 90 days) deep surgical site infection (SSI) following primary total knee arthroplasty (TKA) for osteoarthritis, and to identify risk factors that may be associated with infection. Methods: This is a retrospective population-based cohort study using prospectively collected patient-level data between 1 January 2013 and 1 March 2020. The diagnosis of deep SSI was defined as per the Centers for Disease Control/National Healthcare Safety Network criteria. The Mann-Kendall Trend test was used to detect monotonic trends in annual rates of early-onset deep SSI over time. Multiple logistic regression was used to analyze the effect of different patient, surgical, and healthcare setting factors on the risk of developing a deep SSI within 90 days from surgery for patients with complete data. We also report 90-day mortality. Results: A total of 39,038 patients underwent primary TKA for osteoarthritis during the study period. Of these, 275 patients developed a deep SSI within 90 days of surgery, representing a cumulative incidence of 0.7%. The annual infection rate did not significantly decrease over the seven-year study period (p = 0.162). Overall, 13,885 (35.5%) cases were excluded from the risk analysis due to missing data. Risk factors associated with early-onset deep SSI included male sex, American Society of Anesthesiologists grade ≥ 3, blood transfusion, acute length of stay, and surgeon volume < 30 TKAs/year. Early-onset deep SSI was not associated with increased 90-day mortality. Conclusion: This study establishes a reliable baseline infection rate for early-onset deep SSI after TKA for osteoarthritis using robust Infection Prevention and Control surveillance data, and identifies several potentially modifiable risk factors.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".