Pharmacological interventions for early-stage frozen shoulder: a systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To evaluate the efficacy of pharmacological interventions for treating early-stage, pain predominant, adhesive capsulitis, also known as frozen shoulder. METHODS: We performed a systematic review in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were conducted on MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials on 24 February 2022. Outcomes were shoulder pain, shoulder function and range of movement. Synthesis involved both qualitative analysis for all studies and pairwise meta-analyses followed by a network meta-analysis for randomized controlled trials (RCTs). RESULTS: A total of 3252 articles were found, of which 31 met inclusion criteria, and 22 of these were RCTs. IA injection of CS (8 RCTs, 340 participants) and IA injection of platelet-rich plasma (PRP) (3 RCTs, 177 participants) showed benefit at 12 weeks compared with physical therapy in terms of shoulder pain and function, while oral NSAIDs (2 RCTs, 44 participants) and IA injection of hyaluronate (2 RCTs, 42 participants) did not show a benefit. Only IA PRP showed benefit over physical therapy for shoulder range of movement. CONCLUSION: These results shows that IA CS and IA PRP injections are beneficial for early-stage frozen shoulder. These findings should be appraised with care considering the risk of bias, heterogeneity and inconsistency of the included studies. We believe that research focused on early interventions for frozen shoulder could improve patient outcomes and lead to cost-savings derived from avoiding long-term disability. Further well-designed studies comparing with standardized physical therapy or placebo are required to improve evidence to guide management.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.043 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".