Physical harms associated with suprascapular nerve block interventions in the non-surgical management of acute and chronic shoulder pain: A systematic review
Bibliographic record
Abstract
Background: The utility of the suprascapular nerve block (SSNB) in the non-surgical management of shoulder pain continues to be explored, whilst its associated physical harms have not. This systematic review aims to report the physical harms associated with the SSNB in the non-surgical management of shoulder pain. Methods: A search was undertaken of AMED, CINAHL, Cochrane Library, EMBASE, Medline, Pubmed, and Scopus databases. Studies were included if they reported the presence or absence of harm following a SSNB intervention (injection, pulsed radiofrequency, ablation) in the non-surgical management of acute or chronic shoulder pain. Excluded studies were those which utilised SSNB for peri, intra, or post-surgical intervention. The McMaster tool for assessing quality of harms assessment and reporting was utilised. Results: A total of 111 studies were included in this review of which 168 episodes of harm were reported across 4142 participants. Harm severity ranged from pneumothorax (n = 5) to local pain and bruising (n = 50). The quality of harms assessment and reporting across all studies was poor. Discussion: Despite heterogeneity in SSNB intervention, and low-quality evidence, SSNB carries a low risk of physical harm. Further work is needed in addressing the poor quality of harms assessment and reporting in SSNB studies.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".