Arthroscopic management of synovial chondromatosis of the shoulder: a systematic review of literature
Bibliographic record
Abstract
Background: Synovial chondromatosis (SC) of the shoulder is rare, with limited literature on its management. This systematic review of literature aimed to characterize common arthroscopic techniques for the treatment of shoulder SC and patient outcomes. We hypothesized that arthroscopy is an effective operative modality for the management of shoulder SC. Methods: PubMed and Embase databases were searched for articles on arthroscopic management of shoulder SC, published before 6 August 2020. All articles meeting inclusion criteria received an independent full-text review by two authors. Results: An initial search found 64 articles. Following duplicate removal and title, abstract, and full-text reviews, 27 articles (48 patients) remained eligible. The mean age of patients was 33.0 years, with 2:1 male-to-female ratio. The mean follow-up was 41.8 months. SC was found to affect various intra- and extra-articular locations of the shoulder. Overall, arthroscopic treatment of shoulder SC was successful in 70.8%. Treatment failure was common in SC involving the bicipital tendon sheath. Disease recurrence was seen in 14.7%. Conclusion: Literature on arthroscopic management of shoulder SC is limited, and significant heterogeneity in arthroscopic techniques was observed. Although arthroscopic management of shoulder SC is effective, further optimization is necessary to minimize treatment failure and disease recurrence.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".