A Cadaveric Study Evaluating the Feasibility of an Ultrasound-Guided Diagnostic Block and Radiofrequency Ablation Technique for Sacroiliac Joint Pain
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Ultrasound (US)-guided diagnostic block/radiofrequency ablation (RFA) along the lateral sacral crest (LSC) has been proposed for managing sacroiliac joint (SIJ) pain. We sought to investigate (1) ease of visualization of bony landmarks using US; (2) consistency of US-guided needle placement along the LSC; and (3) percentage of the posterior sacral network (PSN) innervating the SIJ complex that would be captured if an RFA strip lesion were created between the needles. METHODS: In 10 cadaveric specimens, 3 needles were placed bilaterally along the LSC from the first to third transverse sacral tubercles (TSTs) using US guidance. The PSN, SIJ, and needles were exposed, digitized, and modeled 3-dimensionally. Ease of visualization of bony landmarks, frequency of needle placement along the LSC, and percentage of the PSN that would be captured if an RFA strip lesion were created between the needles were determined. RESULTS: The LSC, TST2, TST3, and first to third posterior sacral foramina were easily visualized using US; TST1 was somewhat obscured by the iliac crest in some specimens. Needles were placed along the LSC in 18 of 20 specimens; in the first 2 of 20 specimens, needle 1 was placed at the L5/S1 facet joint. On average, 93% (95% confidence interval, 87%-98%) of the PSN would be captured if an RFA strip lesion were created between the needles. CONCLUSIONS: The findings suggest that US-guided needle placement along the LSC is consistent and could capture most or all of the PSN. A clinical study evaluating the outcomes of this technique is in progress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".