Variability in Recommendations for Total Knee Arthroplasty Among Rheumatologists and Orthopedic Surgeons
Bibliographic record
Abstract
OBJECTIVE: The most rapidly growing population of patients undergoing total knee arthroplasty (TKA) is under the age of 65. The objective of our study was to gain insight into the factors influencing physicians' recommendations for persons in this age group with moderate osteoarthritis (OA). METHODS: Rheumatologists and orthopedic surgeons attending national meetings were asked to complete a survey including a standardized scenario of a 62-year-old person with knee OA who has moderate knee pain limiting strenuous activity despite medical management. We used an experimental 2 × 2 × 2 design to examine the effects of sex, employment status, and severity of radiographic OA on physicians' recommendations. Each physician was asked to rate a single scenario. RESULTS: The percentage of physicians recommending TKA varied from 30% to 55% for scenarios describing a patient with mild radiographic OA, and from 39% to 71% for scenarios describing a patient with moderate radiographic OA. Surgeons were less likely to recommend TKA for women compared to men of the same age, employment status, symptom severity, and functional status, and radiographs. Rheumatologists practicing in academic settings were more likely to recommend TKA compared to those practicing in nonacademic settings, and American surgeons were more likely to recommend TKA compared to their European counterparts. CONCLUSION: Orthopedic surgeons and rheumatologists vary significantly in their recommendations for patients with moderate knee pain and functional limitations. Both patient and physician characteristics influence physicians' recommendations, and rheumatologists and orthopedic surgeons display different patterns of decision making.
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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 | 0.011 | 0.076 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".