Using Surgical Appropriateness Criteria to Examine Outcomes of Total Knee Arthroplasty in a United States Sample
Bibliographic record
Abstract
OBJECTIVE: We determined outcomes for patients classified as appropriate, inconclusive, or inappropriate for total knee arthroplasty (TKA) using a modified version of a validated appropriateness algorithm. Outcome measurement was conceptualized as short-term postoperative change attributable primarily to surgery and rehabilitation (2 months) and as longer-term postoperative change and recovery (1 and 2 years). METHODS: Preoperative and yearly postoperative Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) function, Knee Injury and Osteoarthritis Outcome Score (KOOS) symptoms and KOOS pain scores were examined for persons undergoing primary TKA in the Osteoarthritis Initiative. Multigroup, 2-piece latent growth curve modeling was used to determine differences in outcome variable changes for each group from presurgery to 2-months postsurgery, as well as over a 2-year postoperative period. RESULTS: Data from 167 persons with primary TKA were examined. Prevalence rates of appropriate, inconclusive, and inappropriate judgments were 47.9%, 20.8%, and 31.3%, respectively. The inappropriate group showed no change at 2 months following surgery, while appropriate and inconclusive groups had substantial improvement in all outcomes. One-year and 2-year postoperative recovery outcomes were not significantly different among the 3 groups. CONCLUSION: The inappropriate group was unchanged 2 months after surgery and on average improved by 2.3 WOMAC function points from presurgery to 1 year following surgery based on our models. Appropriate and inconclusive groups improved by an average of 19.8 WOMAC function points at 1-year postsurgery. These data provide a compelling case for consensus-building efforts to define eligibility criteria for TKA with the goals of reducing variation in patient selection and optimizing both change over time and final outcomes.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".