Association between socioeconomic status and patient-reported outcome at 1 year after shoulder arthroplasty for osteoarthritis or cuff-tear arthropathy: a nationwide cohort study of 2,292 arthroplasties
Bibliographic record
Abstract
PURPOSE: We aimed to evaluate the association between socioeconomic factors and patient-reported Western Ontario Osteoarthritis of the Shoulder (WOOS) index at 1 year after hemiarthroplasty, reverse, or anatomical total shoulder arthroplasty for osteoarthritis or cuff-tear arthropathy. METHODS: Eligible patients were identified using linked national data from the Danish Shoulder Arthroplasty Registry and Statistics Denmark between April 2012 and April 2019. Univariable and multivariable linear regression was used to identify the association between socioeconomic factors and the WOOS index at 1 year following primary shoulder arthroplasty adjusted for age, sex, underlying diagnosis, implant design, and comorbidities. We examined socioeconomic factors including employment status, marital status, education, and income. Estimates were provided with 95% confidence intervals (CI). RESULTS: 2,292 patients were identified with a mean WOOS index of 76 (standard deviation 24). In the adjusted analysis, unemployed patients had a significantly lower WOOS index compared with patients with low-level jobs (14, CI 7.0-21), patients with high-level jobs (19, CI 12-25), and retired patients (14, CI 8.3-21). Low education level was associated with a lower WOOS index compared with medium education (4.8, CI 2.6-7.0) and high education level (7.7, CI 5.0-10). There was no association between WOOS index and income or marital status. CONCLUSION: Unemployment and low education level were associated with worse WOOS index 1 year after shoulder arthroplasty for osteoarthritis or cuff-tear arthropathy. This highlights a potential inequity in patient-reported outcomes after shoulder arthroplasty.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".