Three-Dimensional Endoscopic Magnification for Treatment of Thoracic Spinal Dural Arteriovenous Fistulas: Technical Note
Bibliographic record
Abstract
BACKGROUND: Treatment of thoracic spinal dural arteriovenous fistulas (DAVFs) by microsurgery has recently been approached using minimally invasive spine surgery (MISS). The advantages of such an approach are offset by difficult maneuverability within the tubular retractor and by the creation of "tunnel vision" with reduced luminosity to a remote surgical target. OBJECTIVE: To demonstrate how the pitfalls of MISS can be addressed by applying 3-D endoscopy to the minimally invasive approach of spinal DAVFs. METHODS: We present 2 cases of symptomatic thoracic DAVFs that were not amenable to endovascular treatment. The DAVFs were excluded solely via a minimally invasive approach using a 3-D endoscope. RESULTS: Two patients underwent exclusion of a DAVF following laminotomy, one through a midline 5-cm incision and the other through a paramedian 3-cm incision using minimally invasive nonexpandable tubular retractors. The dura opening, intradural exploration, fistula exclusion, and closure were performed solely under endoscopic 3-D magnification. No incidents were recorded and the postoperative course was marked by clinical improvement. Postoperative imaging confirmed the exclusion of the DAVFs. Anatomical details are exposed using intraoperative videos. CONCLUSION: When approaching DAVFs via MISS, replacing the microscope with the endoscope remedies the limitations related to the "tunnel vision" created by the tubular retractor, but at the expense of losing binocular vision. We show that the 3-D endoscope resolves this latter limitation and provides an interesting option for the exclusion of spinal DAVFs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".