Risk factors for the development of degenerative changes among patients undergoing rotator cuff repair: A systematic review
Bibliographic record
Abstract
Background: Osteoarthritis (OA) of the glenohumeral joint results in significant pain and functional limitations. It is unclear which risk factors increase the risk of developing glenohumeral OA amongst Rotator Cuff Repair (RCR) patients. The purpose of this systematic review was to examine the risk factors which may contribute to the development of osteoarthritic changes post-operatively. Methods: MEDLINE, Embase, and PubMed databases were searched to identify studies reporting on demographics of patients who develop OA following RCR. Results: Seventeen articles were identified investigating a total of 1292 patients. The overall quality of evidence was low. Pooled assessment of OA incidence following RCR at minimum 5 years follow-up found 26% of patients developed OA. Patients requiring revision surgery following retears developed OA at a rate of 29%. Surgical technique and patient demographics may also contribute to degenerative changes. Discussion: This review found correlations between the aforementioned risk factors and glenohumeral joint degeneration at long-term follow-up after RCR. These findings suggest that future long-term studies should aim to identify prognostic factors that may place a patient at increased risk of developing OA. Such data can be used to counsel patients with respect to long-term outcomes following surgical intervention.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".