Total Shoulder Replacement vs. Hemiarthroplasty in the Treatment of Shoulder Osteoarthritis
Bibliographic record
Abstract
Total shoulder arthroplasty (TSA) and humeral hemiarthroplasty (HHR) are the two primary methods of treatment for primary should osteoarthritis. Both treatment methods have their advantages and disadvantages. TSA offers better pain relief, range of motion, and patient satisfaction. However, it is a more technically challenging surgery, requiring longer surgical time. It carries the potential risk of glenoid component loosening. Although HHR is technically easier to perform, it has the potential for glenoid erosion which may lead to revision surgery and less optimal outcomes. Recent publications including prospective randomized clinical trials, meta-analysis, and retrospective reviews have indicated that TSA is superior to HHR in treating primary shoulder osteoarthritis in patients with adequate glenoid bone stock, absence of active infection, and intact rotator cuff and deltoid muscles. Furthermore, the rate of revision surgery for TSA is significantly less than in HHR when all-poly glenoid components are used. In conclusion, current evidence suggests that for primary glenohumeral arthritis with appropriate indications, TSA is the surgical treatment of choice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".