Appropriateness and Total Hip Arthroplasty: Determining the Structure of the American Academy of Orthopaedic Surgeons System of Classification
Bibliographic record
Abstract
OBJECTIVE: In late 2017, the American Academy of Orthopaedic Surgeons (AAOS) published an appropriateness classification system using the RAND/University of California, Los Angeles (UCLA) approach for patients with hip osteoarthritis (OA). We determined the contribution of predictor variables in the system to final classification, rated as "appropriate," "may be appropriate," or "rarely appropriate" for hip arthroplasty. METHODS: An AAOS-appointed expert panel developed 270 clinical vignettes incorporating all permutations of 5 evidence-driven indication variables associated with hip arthroplasty outcome or need. Indication variables were age, function-limiting pain severity, radiographic hip OA severity, hip motion, and presence of modifiable prognostic risk factors. Multinomial regression determined the relative contribution of each variable and a classification tree method determined variable combinations contributing to final classification. RESULTS: Patient age and hip OA severity were the dominant predictors of appropriateness classification in both statistical models. Function-limiting pain made a slight contribution relative to age and hip OA severity while hip motion and the presence of modifiable prognostic factors did not meaningfully contribute to final classification. The regression model explained about 99% of the variance and the classification tree had an accuracy of 87.8%. CONCLUSION: Classification for hip arthroplasty appropriateness in the AAOS system is driven almost exclusively by age and OA severity. Function-limiting pain, a major reason patients seek surgery, contributes only slightly to the AAOS appropriateness criteria. The system relies heavily on traditional variables of patient age and radiographic hip OA severity. Future study of actual patient outcomes is needed to further test the validity of the AAOS system.
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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 | 0.028 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".