A Randomized Controlled Trial of the Cost-Effectiveness of Ultrasound-Guided Intraarticular Injection of Inflammatory Arthritis
Bibliographic record
Abstract
OBJECTIVE: We studied whether sonographic needle guidance affected the outcomes of intraarticular (IA) injection for inflammatory arthritis. METHODS: Joints with inflammatory arthritis (n = 244; 76% rheumatoid arthritis, 3% small joints, 51% intermediate, and 46% large) were randomized to injection by conventional palpation-guided anatomic injection (120 joints) or sonographic image-guided injection enhanced with a 1-handed reciprocating procedure device mechanical syringe (124 joints). A 1-needle, 2-syringe technique was used. After IA placement and synovial space dilation were confirmed by sonography, a syringe exchange was performed, and triamcinolone acetonide was injected with the second syringe through the indwelling IA needle. Baseline pain, procedural pain, pain at outcome (2 weeks and 6 months), responders, therapeutic duration, reinjection rates, total cost, and cost per responder were determined. RESULTS: Relative to conventional palpation-guided methods, sonographic guidance for injection of inflammatory arthritis resulted in an 81% reduction in injection pain (p < 0.001), 35% reduction in pain scores at outcome (p < 0.02), 38% increase in the responder rate (p < 0.003), 34% reduction in the non-responder rate (p < 0.003), 32% increase in therapeutic duration (p = 0.01), 8% reduction ($7) in cost/patient/year, and a 33% ($64) reduction in cost/responder/year for a hospital outpatient (p < 0.001). CONCLUSION: Sonographic needle guidance improves the performance, clinical outcomes, and cost-effectiveness of IA injections for inflammatory arthritis. (Clinical Trial Identifier NCT00651625).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".