Bibliographic record
Abstract
In their paper titled “Appropriateness and Total Hip Arthroplasty: Determining the Structure of the American Academy of Orthopaedic Surgeons System of Classification,” Riddle and Perera have analyzed the American Association of Orthopedic Surgeons (AAOS) appropriate-use criteria (AUC) for total hip arthroplasty (THA)1. They aimed to determine the contribution of each of the variables included by the AAOS (age, function-limiting pain, hip radiographic evaluation, range-of-motion limitation, presence or absence of modifiable risk factors) to the classification of appropriateness. An appropriate procedure is commonly defined as one for which “the expected health benefits significantly exceed the expected health risks by a wide margin,” based on the best available evidence2. The aim of AUC is to improve patient care and outcomes, and to identify the complexities of clinical decision making, helping practitioners and patients make a decision about a specific procedure in a specific clinical condition. The US Center for Medicare and Medicaid Services (CMS) established a program to promote AUC in response to both overuse and underuse of medical procedures, and to link them to physician payments (now pushed back to 2020). In response to the CMS and cognizant of wide regional variations in the use of arthroplasty, the significant proportion of recipients who are dissatisfied, and the expenditure of billions of dollars annually, the AAOS developed AUC to guide management of osteoarthritis of the hip, including performance of THA3. Appropriateness differs from guideline recommendations, which provide overarching approaches to healthcare but cannot determine whether the procedure should be performed in an individual patient’s situation. This is where AUC can be used for guidance in decision making, because AUC can identify gradations in severity of disease or risk in specific clinical situations. In a process using the RAND/University of California at Los Angeles Appropriateness Method, the … Address correspondence to Dr. S.M. Goodman, Department of Rheumatology, Hospital for Special Surgery, 535 East 70th St., New York, New York 10021, USA. E-mail: goodmans{at}hss.edu.
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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 | 0.080 | 0.489 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".