Prevalence of Appropriate Anatomic Total Shoulder Arthroplasty in a Large Multicenter US Cohort Using a RAND/UCLA Algorithm
Bibliographic record
Abstract
BACKGROUND: Given the high variability in patient presentation, notable challenges exist in determining patient candidacy for anatomic total shoulder arthroplasty (aTSA). The purpose of this study was to use a modified version of prior scenario-based appropriateness use criteria to evaluate the prevalence of inappropriate, appropriate, and inconclusive aTSA. METHODS: Patients undergoing primary aTSA were evaluated for preoperative outcome scores and baseline demographic information from a multicenter database. Using a validated appropriateness use criteria algorithm, these patients were grouped "inappropriate," "inconclusive," or "appropriate." RESULTS: Seven hundred seventy-four patients who underwent aTSA were included. "Appropriate" patients comprised 23.9% of the cohort, while 17.8% were "inappropriate," and 58.3% were "inconclusive." Compared with the inconclusive and inappropriate groups, the "appropriate" patients were found to have markedly worse preoperative pain and functional outcomes scores. No notable difference was observed between the number of patients who received intra-articular injections, number of injections received, and analgesic use across the groups. CONCLUSIONS: The large proportion of "inconclusive" patients suggests a lack of consensus regarding aTSA versus reverse TSA candidacy and may be secondary to factors such as worse glenoid morphology and/or prior rotator cuff repair, which are subjects of current debate in determining appropriateness for reverse TSA versus aTSA. Although no definitive conclusions can be made regarding if this algorithm ultimately improves patient outcomes, this study seeks to only help streamline patient evaluation based on American Shoulder and Elbow Surgeons high-volume surgeons' opinion and highlight the large variation in the indications for aTSA in real-world surgical cases.
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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 | 0.004 | 0.016 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".