Spinal cord vascular shunts: spinal cord vascular malformations and dural arteriovenous fistulas
Bibliographic record
Abstract
OBJECT: Spinal cord vascular malformations are rare, fascinating lesions. In this paper, the authors' goal was to demonstrate how these lesions, more specifically spinal cord arteriovenous malformations and dural arteriovenous fistulas, are assessed, classified, and managed at their institution. They also highlight some aspects of classification and management that may be different from the views of others. METHODS: The authors reviewed the 20-year experience at the senior author's institution regarding the management of spinal cord vascular malformations. They discuss the management, surgical and endovascular treatment results, and the classification that resulted from the combined experience of 3 major reference centers. RESULTS: The accumulated knowledge on embryological and pathophysiological aspects in such a rare disease resulted in a more global, patient-oriented (and not radiologically oriented) approach to spinal cord shunts. CONCLUSIONS: The multiple classifications proposed for spinal cord vascular malformations reflect the continuous advancement of the authors' understanding. They adopt a classification based on new physiological and genetic data that treats these lesions as expressions of more complex disease processes and not simply a morphological target, with direct impact on therapeutic options.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".