Update on hip and knee arthroplasty: Current state of evidence
Bibliographic record
Abstract
Introduction Total hip (THA) and total knee (TKA) arthroplasties are cost-effective interventions for reducing pain, improving function, and enhancing the quality of life in patients with arthritis of the hip and knee (1,2). More than 193,000 THA and 381,000 TKA procedures are performed in the US each year (3), and future projections indicate that by the year 2030, more than 750,000 of these procedures will be performed per year (4). Therefore, it is imperative for health care professionals to practice evidence-based medicine by integrating the scientific literature with their clinical expertise and the patients’ preferences to select the most effective treatment interventions for enhancing patient recovery after joint replacement (5). In response to this need, the Association of Rheumatology Health Professionals, a division of the American College of Rheumatology, assembled a multidisciplinary group of experts from the US and Canada in a conference to review the current evidence on hip and knee arthroplasty. The purpose of the conference was to provide stateof-the-art information for health care professionals on the surgical procedures, preoperative interventions, biomechanical considerations, rehabilitation strategies, outcomes assessment, and health disparities related to THA and TKA. We present the findings from that conference, which synthesizes the available evidence in each area, identifies the gaps in knowledge, and provides suggestions for future research.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".