Surgical treatment options for articular cartilage defects of the glenohumeral joint: A systematic review
Bibliographic record
Abstract
Background: Many joint-preserving surgical interventions for cartilage defects of the knee have been adapted for use in the shoulder; however, there still exists no clear consensus for treatment. Thus, the purpose of this systematic review was to evaluate the outcomes of different interventions in patients with focal chondral lesions of the glenohumeral joint. Methods: A literature search was conducted using PubMed, Embase, and Medline. Patients who underwent a joint-preserving surgical procedure to treat a focal chondral defect of the glenoid, humeral head or both were included. Patients treated for diffuse cartilage defects or with shoulder arthroplasty were excluded. Results: Ten studies were included, with follow-up data available for 194 shoulders. Eight joint-preserving procedures were evaluated, with microfracture being the most common. One study evaluating microfracture reported significant improvements in patient-reported outcomes at short-term and long-term follow-up compared to preoperative scores. Across all studies, 32 patients underwent subsequent shoulder surgery, with 22 being arthroplasties. Conclusions: We found improvements in patient-reported and functional outcomes across all studies. Although joint-preserving procedures have shown reasonable outcomes for focal chondral defects of the glenohumeral joint, long-term outcomes remain unknown, and the progression of osteoarthritis remains a concern. Higher quality evidence is required to make definitive recommendations. Level of Evidence: IV.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.005 | 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".