The economic impact of periprosthetic infection in total knee arthroplasty
Bibliographic record
Abstract
Background: Currently, the gold standard treatment for periprosthetic joint infection (PJI) after total knee arthroplasty (TKA) is 2-stage revision, but few studies have looked at the economic impact of PJI on the health care system. The objective of this study was to obtain an accurate estimate of the institutional cost associated with the management of PJI in TKA and to assess the economic impact of PJI after TKA compared to uncomplicated primary TKA. Methods: We identified consecutive patients in our institutional database who had undergone 2-stage revision TKA for PJI between 2010 and 2014 and matched them on age and body mass index with patients who had undergone uncomplicated primary TKA over the same period. We calculated all costs associated with the 2 procedures and compared mean costs, length of stay, clinical visits and readmission rates between the 2 groups. Results: There were 73 patients (mean age 68.8 [range 48-91] yr) in the revision TKA cohort and 73 patients (mean age 65.9 [range 50-86] yr) in the primary TKA cohort. Two-stage revision surgery was associated with a significantly longer hospital stay (mean 22.7 d v. 3.84 d, p < 0.001), more outpatient clinic visits (mean 8 v. 3, p < 0.001), more readmissions (29 v. 0, p < 0.001) and higher overall cost (mean $35 429.97 v. $6809.94, p < 0.001) than primary TKA. Conclusion: Treatment for PJI after TKA has an enormous economic impact on the health care system. Our data suggest a fivefold increase in expenditure in the management of this complication compared to uncomplicated primary TKA.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".