The Causes and Costs of Surgical Site Infection in Total Hip and Total Knee Arthroplasty: A Retrospective Review of 4,973 Procedures.
Bibliographic record
Abstract
Background: The purpose of this article is to delineate risk factors associated with SSI (surface, deep tissue, and periprosthetic joint infections) in hip and knee total joint replacement (TJR) surgeries for both primary and revision procedures. Methods: Retrospective case-control study of non-emergent TJR procedures performed at a tertiary level academic medical center between 2014-2018. Multivariable logistic regression was used to determine which factors are associated with an increased risk for SSI in TJR. Results: 4,973 procedures (2,543 knee and 2,430 hip arthroplasties) were performed on 4,014 unique patients. There were 82/4,973 total SSI: 43/2,430 (1.8%) in the THA group and 39/2543 (1.5%) in the TKA group. Risk factors associated with the development of an SSI included a female gender (65% increased odds ratio), BMI (increased odds ratio 3% for every 1-point increase in BMI (10-point BMI increase = 30% increased odds), length of surgery (8% increase for every additional 10 minutes of surgical time). Chronic renal disease and anemia double the odds of an SSI and cardiac arrythmias increased the odds by 88%. A history of skin integrity issues more than doubled the odds and a previous skin ulcer more than tripled the odds of an SSI. Using a multi-layered dressing reduces the odds and not using one more than doubles the odds of suffering an SSI. An SSI increased length of stay by two days and cost of stay by $38,000. Conclusion: .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".