Validation of a second-generation appropriateness classification system for total knee arthroplasty: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: To test the validity of a second-generation appropriateness system in a cohort of patients undergoing total knee arthroplasty (TKA). METHODS: We applied the RAND/UCLA Appropriateness Method to derive our second-generation system and conducted a prospective study of patients diagnosed with knee osteoarthritis in eight public hospitals in Spain. Main outcome questionnaires were the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Short-Form-12 (SF-12), and the Knee Society Score satisfaction scale (KSS), completed before and 6 months after TKA. Baseline, changes from baseline to 6 months (journey outcome), and 6-month scores (destination outcome) were compared according to appropriateness category. Percentage of patients attaining the minimal clinically important difference (MCID) and responders according to Outcome Measures in Rheumatology-Osteoarthritis Research Society (OMERACT-OARSI) criteria were also reported. RESULTS: A total of 282 patients completed baseline and 6-month questionnaires. Of these, 142 (50.4%) were classified as Appropriate, 90 (31.9%) as Uncertain, and 50 (17.7%) as Inappropriate. Patients classified as Appropriate had worse preoperative pain, function, and satisfaction (p < 0.001) and had greater improvements (i.e., journey scores) than those classified as Inappropriate (p < 0.001). At 6 months, destination scores for pain, function, or satisfaction were not significantly different across appropriateness categories. The percentage of patients meeting responder criteria (p < 0.001) and attaining MCID was statistically higher in Appropriate versus Inappropriate groups in pain (p = 0.04) and function (p = 0.004). CONCLUSIONS: The validity of our second-generation appropriateness system was generally supported. The findings highlight a critical issue in TKA healthcare: whether TKA appropriateness should be driven by the extent of improvement, by patient final state, or by both.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".