Comparison of Fluoroscopy and Ultrasound Guidance for Sacroiliac Joint Injection in Patients with Chronic Low Back Pain
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sacroiliac joint (SIJ) arthritis is a common cause of chronic mechanical low back pain (LBP) that is often treated with injection of local anesthetic and steroids. Ultrasound (US) has emerged as a viable alternative to fluoroscopy (FL) to guide SIJ injections; however, few studies have compared these modalities. In this prospective randomized, controlled trial, we compared both accuracy and efficacy of US and FL guidance for SIJ injections. METHODS: Forty patients with chronic moderate-to-severe LBP secondary to SIJ arthritis were randomized to receive US- or FL-guided unilateral SIJ injections. Primary outcomes included pain at 1 month measured by numerical rating scale (NRS) scores. Secondary outcomes included NRS scores at 24 hours, 72 hours, 1 week, and 3 months after injection, physical functioning at 1 month after the procedure, procedure time, incidence of intra-articular and peri-articular needle placement, patient discomfort, overall patient satisfaction, and daily opioid consumption. RESULTS: There was no significant difference in NRS pain scores between the 2 groups at 1 month or at any other follow-up points. A significant reduction from baseline mean NRS scores was observed in both groups at 1 month after injection (US 22.7%, P = 0.025; FL 37.3%, P < 0.001). There was no significant difference in procedure-related variables, physical functioning, discomfort, opioid utilization, and patient satisfaction between the 2 groups. CONCLUSIONS: Ultrasound-guided SIJ injection with fluoroscopic confirmation has similar accuracy and efficacy to fluoroscopy alone for SIJ injections in patients with chronic low back pain secondary to SIJ arthritis.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".