Use of a Validated Algorithm to Judge the Appropriateness of Total Knee Arthroplasty in the United States: A Multicenter Longitudinal Cohort Study
Bibliographic record
Abstract
OBJECTIVE: In previous studies conducted outside the US, ∼20% of total knee arthroplasty (TKA) surgeries were judged to be inappropriate. The present study was undertaken to determine the prevalence rates of TKA surgeries classified as appropriate, inconclusive, and inappropriate in a knee osteoarthritis population in the US. METHODS: We used a modification of a validated appropriateness classification system and applied it to patients in the Osteoarthritis Initiative data set who underwent TKA. A variety of preoperative data were used in the classification, including Western Ontario and McMaster Universities Osteoarthritis Index pain and physical function scores, radiographic features, knee motion and laxity measures, and age. RESULTS: Data on 205 patients who underwent TKA were examined. The prevalence rates for classification of the procedure as appropriate, inconclusive, and inappropriate were 44.0% (95% confidence interval [95% CI] 37-51%), 21.7% (95% CI 16-28%), and 34.3% (95% CI 27-41%), respectively. CONCLUSION: Approximately one-third of TKA surgeries were judged to be inappropriate. Variation in the characteristics of patients undergoing TKA was extensive. These data support the need for consensus development of criteria for patient selection among US practitioners treating patients who are potential candidates for TKA. Among the important issues, consensus development needs to address variation in patient characteristics and the relative importance of preoperative status and subsequent outcome.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".