What is a Successful Outcome Following Reverse Total Shoulder Arthroplasty?
Bibliographic record
Abstract
BACKGROUND: With variations in joint destruction, patient expectations and health status, it can be difficult to interpret outcomes following arthroplasty. The purpose of this study was to determine the relationships between different outcome indicators in 44 patients followed for two years after a reverse shoulder arthroplasty. METHODS: Prospectively collected outcomes included the Constant-Murley score, Simple Shoulder Test (SST), range of motion (ROM), strength, patient satisfaction with their care and independent clinician case-review to determine global clinical outcome. Continuous outcomes were divided in two subgroups according to definitions of functional outcomes. Cohen's kappa was used to evaluate agreement between outcomes. Pearson correlations were used to quantify interrelationships. RESULTS: Although 93% of patients were substantially satisfied, fewer had good results on the other outcomes: 68% on global clinical outcome, 46% on SST and 73% on Constant-Murley score. The SST demonstrated better than chance agreement with Constant-Murley score, ROM in flexion, abduction and external rotation, and strength in external rotation. No agreement between satisfaction and other outcomes were observed. Significant correlations were observed between Constant-Murley score and SST (r = 0.78). The Constant-Murley score and SST demonstrated variable correlation with ROM and strength in flexion, abduction, internal and external rotation (0.38 < r < 0.73); the highest correlations being observed with shoulder elevation ROM (r > 0.50). CONCLUSIONS: Results show that outcome varies according to patient perspective and assessment methods. Patient satisfaction with their care was related to neither self-reported nor physical impairment outcomes. Positive patient ratings of satisfaction may not necessarily be evidence of positive 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".