Non-operative orthobiologic use for rotator cuff disorders and glenohumeral osteoarthritis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Shoulder pain from rotator cuff pathology and glenohumeral osteoarthritis is a common entity encountered in musculoskeletal practices. Orthobiologic agents are being increasingly used as a treatment option and understanding their safety and efficacy is necessary. OBJECTIVE: To systematically evaluate the available evidence for orthobiologic use in rotator cuff and glenohumeral pathology. METHODS: A systematic review was undertaken following PRISMA guidelines. Randomized clinical trials (RCTs) and prospective cohort studies evaluating non-operative treatment with prolotherapy, platelet-rich plasma (PRP), or medicinal signaling cells (MSCs) for rotator cuff pathology and glenohumeral osteoarthritis were included. Bias risk assessments used were the Cochrane tool and Newcastle-Ottawa score. RESULTS: The search yielded 852 potential articles, of which 20 met the inclusion criteria with a breakdown of 5 prolotherapy, 13 PRP, and 2 MSC. Sixteen studies were RCTs and 4 were cohort studies. Six studies were deemed "low risk of bias or good quality". Efficacy results were mixed, and no serious adverse events were reported from orthobiologic treatment. CONCLUSIONS: Orthobiologics offer a relatively safe management option with inconclusive evidence for or against its use for rotator cuff pathology. No studies on glenohumeral osteoarthritis met the inclusion criteria. Adoption of standardized preparation reporting and consistent use of functional outcome measures is imperative for future studies to consider.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".