Previous surgery for instability is a risk factor for a worse patient-reported outcome after anatomical shoulder arthroplasty for osteoarthritis: a Danish nationwide cohort study of 3,743 arthroplasties
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although most patients have good outcomes after shoulder arthroplasty for osteoarthritis, certain risk factors may lead to disappointing outcomes. We assessed risk factors for a worse outcome after anatomical shoulder arthroplasty for osteoarthritis. Our hypothesis was that previous surgery for instability would be a risk factor for a worse outcome independent of age, sex, and arthroplasty type. PATIENTS AND METHODS: We included arthroplasties reported to the Danish Shoulder Arthroplasty Registry between 2006 and 2018 (n = 3,743). The Western Ontario Osteoarthritis of the Shoulder (WOOS) index at 1 year was used as outcome. The total score was converted to a percentage of a maximum score. The general linear model was used to analyze differences in WOOS. Age, sex, arthroplasty type, and previous surgery were included in the multivariate model. Estimates were given with 95% confidence intervals (CI). RESULTS: The mean WOOS score was 78 for patients with no previous surgery and 55 for patients with surgery for instability. The mean difference was -16 (CI -10 to -22) in the multivariate model. Hemiarthroplasty had a worse outcome compared with total shoulder arthroplasty and young patients had a worse outcome compared with older patients. The mean differences in the multivariate model were -12 (CI -10 to -14) and -11 (CI -8 to -14) respectively. INTERPRETATION: Patients with previous surgery for instability had worse results independent of age, sex, and arthroplasty type and should be informed about their individual risk of a worse outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".