First-Pass Contrast-Enhanced MRA for Pretherapeutic Diagnosis of Spinal Epidural Arteriovenous Fistulas with Intradural Venous Reflux
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Spinal epidural AVFs are rare spinal vascular malformations. When there is associated intradural venous reflux, they may mimic the more common spinal dural AVFs. Correct diagnosis and localization before conventional angiography is beneficial to facilitate treatment. We hypothesize that first-pass contrast-enhanced MRA can diagnose and localize spinal epidural AVFs with intradural venous reflux and distinguish them from other spinal AVFs. MATERIALS AND METHODS: Forty-two consecutive patients with a clinical and/or radiologic suspicion of spinal AVF underwent MR imaging, first-pass contrast-enhanced MRA, and DSA at a single institute (2000-2015). MR imaging/MRA and DSA studies were reviewed by 2 independent blinded observers. DSA was used as the reference standard. RESULTS: On MRA, all 7 spinal epidural AVFs with intradural venous reflux were correctly diagnosed and localized with no interobserver disagreement. The key diagnostic feature was arterialized filling of an epidural venous pouch with a refluxing radicular vein arising from the arterialized epidural venous system. CONCLUSIONS: First-pass contrast-enhanced MRA is a reliable and useful technique for the initial diagnosis and localization of spinal epidural AVFs with intradural venous reflux and can distinguish these lesions from other spinal AVFs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".