Determining the Need for Hip and Knee Arthroplasty: The Role of Clinical Severity and Patients??? Preferences
Bibliographic record
Abstract
BACKGROUND: Area variation in the use of surgical interventions such as arthroplasty is viewed as concerning and inappropriate. OBJECTIVES: To determine whether area arthroplasty rates reflect patient-related demand factors, we estimated the need for and the willingness to undergo arthroplasty in a high- and a low-use area of Ontario, Canada. RESEARCH DESIGN: Population-based mail and telephone survey. SUBJECTS: All adults aged > or =55 years in a high (n = 21,925) and low (n = 26,293) arthroplasty use area. MEASURES: We determined arthritis severity and comorbidity with questionnaires, established the presence of arthritis with examination and radiographs, and evaluated willingness to have arthroplasty with interviews. Potential arthroplasty need was defined as severe arthritis, no absolute contraindication for surgery, and evidence of arthritis on examination and radiographs. Estimates of need were then adjusted for patients' willingness to undergo arthroplasty. RESULTS: Response rates were 72.0% for questionnaires and interviews. The potential need for arthroplasty was 36.3/1,000 respondents in the high-rate area compared with 28.5/1,000 in the low-rate area (P <0.0001). Among individuals with potential need, only 14.9% in the high-rate area and 8.5% in the low-rate area were definitely willing to undergo arthroplasty (P = 0.03), yielding adjusted estimates of need of 5.4/1,000 and 2.4/1,000 in the high- and low-rate areas, respectively. CONCLUSIONS: Demonstrable need and willingness were greater in the high-rate area, suggesting these factors explain in part the observed geographic rate variations for this procedure. Among those with severe arthritis, no more than 15% were definitely willing to undergo arthroplasty, emphasizing the importance of considering both patients' preferences and surgical indications when evaluating need and appropriateness of rates for 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".