Ultrasound-guided shoulder girdle injections are more accurate and more effective than landmark-guided injections: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy and efficacy of ultrasound (US)-guided injections versus landmark-guided injections of the subacromial space, biceps tendon sheath, acromioclavicular (AC) joint and glenohumeral (GH) joint. METHODS: PubMed, Medline and Cochrane libraries were searched up to 31 July 2013. Two independent authors selected and evaluated randomised controlled trials comparing the accuracy and/or efficacy of US versus landmark-guided injection of the shoulder girdle. A meta-analysis of accuracy, pain Visual Analog Scale (VAS), function score and reported adverse events were performed. RESULT: Four cadaveric studies (300 cadaveric shoulders) and nine live human studies (514 patients) were reviewed. Based on three studies for AC joint, the accuracy of US versus a landmark-guided injection was 93.6% vs 68.2% (p<0.0001). Based on single studies, the accuracy of US versus a landmark-guided injection was 65% vs 70% for the subacromial space (p>0.05), 86.7% vs 26.7% for the biceps tendon sheath (p<0.05), and 92.5% vs 72.5% for the GH joint (p=0.025). Based on three studies for the subacromial space, the US group had a significantly greater reduction in pain (mean difference (MD)=1.47, 95% CI 1.0 to 1.93), and improvement in function (standardised MD=0.70, 95% CI 0.39 to 1.01) at 6 weeks postinjection. Based on a single study for the biceps tendon sheath, the US group had a significantly greater reduction in pain (MD 1.9, 95% CI 1.2 to 2.6) and improvement in function (MD=10.9, 95% CI 6.57 to 15.23). CONCLUSIONS: US-guided injections showed greater accuracy for all shoulder girdle injections, with the exception of the subacromial space. There was improved efficacy for the subacromial space and biceps tendon sheath injections.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.028 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".