Good long-term patient-reported outcome after shoulder arthroplasty for cuff tear arthropathy
Bibliographic record
Abstract
Background The use of the reverse shoulder arthroplasty (RSA) for cuff tear arthropathy (CTA) has increased within the last decades, but there is still limited information about the long-term outcome and how it performs in comparison with hemiarthroplasty (HA). The aim of this study was to compare the long-term patient-reported outcomes of RSA and HA for CTA. Methods We included all patients with CTA, who according to the Danish Shoulder Arthroplasty Registry, underwent either HA or RSA between 2006 and 2010. Patients who were alive were sent the Western Ontario Osteoarthritis of the Shoulder (WOOS) questionnaire in 2020. One hundred twenty (65%) patients returned a complete questionnaire. The linear regression model was used to compare RSA and HA. Sex, age, and previous surgery were included in the multivariable model. Results Forty-two HAs and 78 RSAs were evaluated with a mean follow-up time of 11.5 and 10.6 years, respectively. The mean WOOS score was 66.7 for HA and 71.7 for RSA. The difference of 5.0 was neither statistically significant nor clinically important (95% confidence interval: -4.3 to 14.2, P = .17), nor were there any significant risk of a worse WOOS score for sex, age, or previous surgery. Conclusion To our knowledge, this is the first study to compare the long-term patient-reported outcomes of HA and RSA for CTA. Our results indicate that RSA is a reliable and durable treatment option for CTA with good long-term results. Based on this observational study, it is not possible to make safe estimates about the effect of RSA compared with HA, but similar to RSA, HA was associated with relatively good long-term results.
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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 | 0.015 |
| 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 | 0.001 |
| 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".