Dilated Vein of the Filum Terminale on MRI: A Marker for Deep Lumbar and Sacral Dural and Epidural Arteriovenous Fistulas
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Conventional MR imaging can provide important clues regarding the location of a spinal vascular malformation. We hypothesized that a dilated vein of the filum terminale, identified as a curvilinear flow void on T2WI, could be an imaging marker for a lower lumbar (L3-L5) or sacral fistula. MATERIALS AND METHODS: We retrospectively identified all spinal dural and spinal epidural arteriovenous fistulas from 2 large tertiary referral centers from 2005 to 2018. All patients had a lumbar spinal MR imaging and a conventional spinal angiography. Images were reviewed by 2 neuroradiologists who categorized the level of the arterial feeder to the fistula and the presence or absence of a dilated vein of the filum terminale on T2WI and T1 postcontrast images. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value of the presence of a dilated filum terminale vein for a deep lumbar or sacral fistula. RESULTS: One hundred sixty-two patients were included. An enlarged filum terminale vein was identified in 39 patients. Sensitivity, specificity, positive predictive value, and negative predictive value of the presence of a dilated filum terminale vein for a deep lumbar or sacral fistula were 86%, 98.3%, 94.9%, and 95.1%, respectively. CONCLUSIONS: The presence of a dilated vein of the filum terminale can accurately localize a spinal dural arteriovenous fistula/spinal epidural arteriovenous fistula to the lower lumbar or sacral spine in patients being evaluated for such lesions. This finding can be used to facilitate both noninvasive and conventional spinal angiography.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".