Use of a Validated Algorithm to Judge the Appropriateness of Total Knee Arthroplasty in the United States: A Multicenter Longitudinal Cohort Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it