Anatomic total shoulder arthroplasty for osteoarthritis in patients who are 75 years or older. An analysis of revision rates and patient-reported outcome using data from the Danish shoulder arthroplasty registry
Bibliographic record
Abstract
Background: Anatomic total shoulder arthroplasty (aTSA) has been proven effective in the treatment of painful end-stage osteoarthritis in patients with intact rotator cuff function. However, elderly patients may have an increased risk of revision, especially when it comes to the risk of revision due to loosening, rotator cuff pathology and periprosthetic fracture. The aim of this study was to investigate revision rates and patient-reported outcomes after aTSA for osteoarthritis in patients who are 75 years or older. We hypothesized that revision rates and patient-reported outcomes were similar for patients aged 55-74 years and 75 years or older. Methods: This is a registry-based cohort study with data from the Danish Shoulder Arthroplasty Registry. For analysis, 1,884 aTSAs used for osteoarthritis between January 1, 2012, and December 31, 2019, were included. Patients were divided into 2 age groups: 55-74 years and 75 years or older. The Kaplan-Meier method was used to estimate unadjusted cumulative revision rates and a multivariate Cox regression model was used to determine hazard ratios. A multivariable linear regression model was used to compare the Western Ontario Osteoarthritis of the Shoulder Index (WOOS) 1 year postoperatively. Results: = .40). Conclusion: We found low short-term revision rates and good patient-reported outcomes for both middle-aged and older patients treated for osteoarthritis using aTSA. The small differences in WOOS between the 2 age groups were not clinically relevant nor statistically significant. aTSA provides good and reliable outcomes in elderly patients with end-stage osteoarthritis, and age alone should not be a reason for opting out aTSA.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".