The economic impact of periprosthetic infection in total hip arthroplasty
Bibliographic record
Abstract
Background: Periprosthetic joint infection (PJI) is the third leading cause of total hip arthroplasty (THA) failure. Although controversial, 2-stage revision remains the gold standard treatment for PJI in most situations. To date, there have been few studies describing the economic impact of PJI in today’s health care environment. The purpose of the current study was to obtain an accurate estimate of the institutional cost associated with the management of PJI in THA and to assess the economic burden of PJI compared with primary uncomplicated THA. Methods: We conducted a review of primary THA cases and 2-stage revision THA for PJI at our institution. Patients were matched for age and body mass index. All costs associated with each procedure were recorded. Descriptive statistics were used to summarize the collected data. Mean costs, length of stay, clinic visits and readmission rates associated with the 2 cohorts were compared. Results: Fifty consecutive cases of revision THA were matched with 50 cases of uncomplicated primary THA between 2006 and 2014. Compared with the primary THA cohort, PJI was associated with a significant increase in mean length of hospital stay (26.5 v. 2.0 d, p < 0.001), mean number of clinic visits (9.2 v. 3.8, p < 0.001), number of readmissions (12 v. 1, p < 0.001) and average overall cost (Can$38 107 v. Can$6764, t = 8.3, p < 0.001). Conclusion: Treatment of PJI is a tremendous economic burden. Our data suggest a 5-fold increase in hospital expenditure in the management of PJI compared with primary uncomplicated THA.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".