Anatomical Comparison of Radiofrequency Ablation Techniques for Sacroiliac Joint Pain
Bibliographic record
Abstract
Objective: To compare the percentage of sacral lateral branches (LBs) that would be captured if lesions were created by seven current sacroiliac joint (SIJ) radiofrequency ablation (RFA) techniques: three monopolar and four bipolar. Design: Cadaveric fluoroscopy study. Setting: Anatomy and surgical skills laboratories. Subjects: Forty cadaveric SIJs. Methods: LBs were exposed, radiopaque wires were sutured to LBs, and anterior-posterior fluoroscopic images through the S1 superior endplate were obtained. Lesions that would be created by 17 versions of seven current SIJ RFA techniques were mapped on the fluoroscopic images. These 17 versions were compared: 1) percentage of LBs that would be captured; 2) percentage of SIJ specimens in which 100% of LBs would be captured; and 3) percentage of LBs that would not be captured at each level (S1-S4). Results: Both the mean LB and 100% capture rates were greater for the bipolar techniques (93.4-99.7% and 62.5-97.5%, respectively) than for the monopolar techniques (49.6-99.1% and 2.5-92.5%, respectively) evaluated. For the bipolar techniques, 1.5-29.2% of LBs would not be captured at S1 and 0% at S2-S4 vs 0-29.2% at S1-S4 for the cooled monopolar techniques vs 36.9-100% at S1-S4 for the conventional monopolar technique. Conclusions: The findings suggest that, if lesions were created, the RFA needle placement locations of the bipolar techniques evaluated may be capable of capturing all LBs, but those of the current monopolar techniques evaluated may not. Future in vivo imaging studies are required to compare the lesion morphology generated by different SIJ RFA techniques and correlate the findings with clinical outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".