Does Sonographic Needle Guidance Affect the Clinical Outcome of Intraarticular Injections?
Bibliographic record
Abstract
OBJECTIVE: This randomized controlled study addressed whether sonographic needle guidance affected clinical outcomes of intraarticular (IA) joint injections. METHODS: In total, 148 painful joints were randomized to IA triamcinolone acetonide injection by conventional palpation-guided anatomic injection or sonographic image-guided injection enhanced with a one-handed control syringe (the reciprocating device). A one-needle, 2-syringe technique was used, where the first syringe was used to introduce the needle, aspirate any effusion, and anesthetize and dilate the IA space with lidocaine. After IA placement and synovial space dilation were confirmed, a syringe exchange was performed, and corticosteroid was injected with the second syringe through the indwelling IA needle. Baseline pain, procedural pain, pain at outcome (2 weeks), and changes in pain scores were measured with a 0-10 cm visual analog pain scale (VAS). RESULTS: Relative to conventional palpation-guided methods, sonographic guidance resulted in 43.0% reduction in procedural pain (p < 0.001), 58.5% reduction in absolute pain scores at the 2 week outcome (p < 0.001), 75% reduction in significant pain (VAS pain score > or = 5 cm; p < 0.001), 25.6% increase in the responder rate (reduction in VAS score > or = 50% from baseline; p < 0.01), and 62.0% reduction in the nonresponder rate (reduction in VAS score < 50% from baseline; p < 0.01). Sonography also increased detection of effusion by 200% and volume of aspirated fluid by 337%. CONCLUSION: Sonographic needle guidance significantly improves the performance and outcomes of outpatient IA injections in a clinically significant manner.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".