Third‐Degree Burn from Cooled Radiofrequency Ablation of Medial Branch Nerves for Treatment of Thoracic Facet Syndrome
Bibliographic record
Abstract
Radiofrequency ablation of medial branch nerves is considered a safe and effective treatment for chronic facet joint pain in the cervical, thoracic, and lumbosacral spine. Cooled radiofrequency ablation (C-RFA) is gaining popularity over conventional thermal radiofrequency ablation (RFA) in pain management. However, complications of C-RFA have not been reported in the literature. We present a first report of third-degree skin burn resulting from C-RFA electrode use for the treatment of facet syndrome. A 61-year-old woman (BMI of 21.8 kg/m(2) ) with thoracic facet syndrome underwent C-RFA of the T1-4 medial branch nerves (Thoracool System, Baylis Medical Company, Montreal, QC, Canada). Lesioning at the superior-lateral aspect of the thoracic transverse processes at each level was performed. During lesioning of the T2 MBN on the T3 transverse process, skin blanching 15 mm in diameter was noted around the introducer needle with patient complaints of severe, localized pain. Postprocedurally the skin injury at this level worsened in appearance, with a 20 mm × 4 mm skin defect, which took nearly 5 months to heal. With C-RFA, internally cooled electrodes are capable of creating large volume spherical lesions, a size advantage over conventional RFA. Although C-RFA lesion size may overcome the anatomic variability of target nerve location and potentially improve pain outcomes, added vigilance is required in thin patients and in anatomic regions of minimal subcutaneous tissue between the lesion target and the dermis. Skin burns at the site of the RF electrode are a potential risk under such conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".