Predictors of Clinical Benefits and One-Year Functional Outcomes Following Shoulder Arthroplasty.
Bibliographic record
Abstract
Background: Shoulder arthroplasty has been shown to improve function in patients with advanced shoulder disease. However, the response to surgery and final outcomes are not easily predictable. This study assessed the effect of residual pain, age, sex, diabetes, hypertension, and depression on changes and status at one-year following arthroplasty with respect to shoulder function and overall physical and mental health status. Methods: A retrospective analysis of a prospective cohort of 140 patients tested preoperatively and one-year following shoulder arthroplasty was conducted at our tertiary hospital. Pearson's correlations and multiple regression analysis were performed to test the impact of predictors on shoulder pain and function assessed using the American Shoulder and Elbow Surgery (ASES) questionnaire, and on physical and mental health assessed using the Short Form-12. Results: < .05) and was less predictable for physical health change scores (12%) and the physical status at one-year (14%). Conclusions: III.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".