Evaluation of Two Appropriateness Criteria for Total Knee Replacement
Bibliographic record
Abstract
OBJECTIVE: Insurance expansion under the Affordable Care Act will amplify a projected 6-fold increase in total knee replacement (TKR) utilization by 2030 but will not fully address TKR disparities. Promoting appropriate use of TKR would help reduce disparities and improve outcomes. There are currently no validated appropriateness criteria (AC) for TKR in the US. We evaluated the performance of 2 non-US AC in a cohort of US TKR patients. METHODS: AC1 was developed in Spain using the modified Delphi method with 624 patient scenarios. AC2 was developed in Canada using the overall Western Ontario and McMaster Universities Osteoarthritis Index score of >39 as the cutoff point for surgery. These criteria were applied to a random sample of TKR patients enrolled in our institutional registry. Preoperative clinical, radiographic, and patient-reported survey data were used in classifying patients. The rate of appropriateness was compared for the 2 AC. Inappropriate cases were further investigated to determine other mitigating factors beyond the criteria influencing the decision to operate. RESULTS: In total, 508 TKR procedures were evaluated. All patients had osteoarthritic radiographic changes. On the basis of AC1, 7.7% of cases were classified as inappropriate and 11.6% uncertain. On the basis of AC2, 31.5% were classified as inappropriate. Only 4.7% of the cases were classified as inappropriate by both ACs; however, there was poor agreement between the 2 AC (κ = -0.08). Beyond the criteria, failure of nonsurgical treatment and clinically significant valgus/varus deformities influenced the decision for surgery. CONCLUSION: There was poor agreement between 2 validated AC for TKR when tested in a US population. Culturally specific AC are needed to promote rational use of TKR.
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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 | 0.018 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".