Clinical and Economic Burden of Revision Knee Arthroplasty
Bibliographic record
Abstract
Surgery is indicated for symptomatic knee osteoarthritis (OA) when conservative measures are unsuccessful. High tibial osteotomy (HTO), unicompartmental knee arthroplasty (UKA), and total knee arthroplasty (TKA) are surgical options intended to relieve knee OA pain and dysfunction. The choice of surgical intervention is dependent on several factors such as disease location, patient age, comorbidities, and activity levels. Regardless of surgical treatment, complications such as infection, loosening or lysis, periprosthetic fracture, and postoperative pain are known risks and are indications for revision surgery. The clinical and economic implications for revision surgery are underappreciated. Over 55,000 revision surgeries were performed in 2010 in the US, with 48% of these revisions in patients under 65 years. Total costs associated with each revision TKA surgery have been estimated to be in excess of $49,000. The current annual economic burden of revision knee OA surgery is $2.7 billion for hospital charges alone. By 2030, assuming a 5-fold increase in the number of revision procedures, this economic burden will exceed $13 billion annually. It is appealing to envision a therapy that could delay or obviate the need for arthroplasty. From an actuarial standpoint, this would have the theoretical downstream effect of substantially reducing the number of revision procedures. Although no known therapies currently meet these criteria, such a breakthrough would have a tremendous impact in lessening the clinical and economic burden of knee OA revision surgery.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".