Proposed Optimal Fluoroscopic Targets for Cooled Radiofrequency Neurotomy of the Sacral Lateral Branches to Improve Clinical Outcomes: An Anatomical Study
Bibliographic record
Abstract
Background: Current sacroiliac joint (SIJ) cooled radiofrequency (RF) is based on fluoroscopic anatomy of lateral branches (LBs) in three specimens. Recent studies confirm significant variation in LB positions. Objectives: To determine if common fluoroscopic needle placements for cooled SIJ RF are adequate to lesion all S1-3 LBs. If not, would different targets improve lesion accuracy? Methods: The LBs of 20 cadavers were dissected bilaterally (40 SIJs), and 26 G radiopaque wires were sutured to the LBs. With a 10-mm radius ruler centered at each foramen, standard targets were assessed, as judged by a clockface on the right, for S1 and S2 at 2:30, 4:00, and 5:30 positions and at S3 at 2:30 and 4:00. Mirror image targets were assessed on the left. Assuming an 8-mm lesion diameter, the percentage of LBs that would not be ablated for each level was determined. Imaging through the superior end plate of S1 was compared against segment specific (SS) imaging. Results: Nine point four percent of LBs would not be ablated at S1 vs 0.99% at S2 vs 35% at S3, and 60% of the 40 SIJs would be completely denervated using current targets. SS imaging did not improve results. Alternate target locations could improve the miss rate to 2.8% at S1 and 0% at S3 and would ablate all LBs in 95% of SIJs. Conclusions: Using a conservative 8-mm lesion measurement, contemporary cooled RF needle targets are inadequate to lesion all target LBs. Modifications to current targets are recommended to increase the effectiveness of the procedure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".