Classification of Spinal Vascular Malformations
Bibliographic record
Abstract
Spinal vascular malformations are rare diseases with a wide variety of neurological presentations. Their classification depends on the differentiation of shunting versus non-shunting lesions, the latter being the spinal cord cavernomas. In the shunting lesions, the next step in the proposed classification scheme is related to the feeding artery which can subdivide the dural vascular shunts from the pial vascular malformations: while those shunts that are fed by radiculomeningeal arteries (i.e. the counterparts of meningeal arteries in the brain) constitute the dural arteriovenous fistulas, the shunts that are fed by arteries that would normally supply the spinal cord (i.e. the radiculomedullary and radiculopial arteries) are the pial cord arteriovenous malformations (whose cranial counterparts are the brain AVMs). Depending on the type of transition between artery and vein the latter pial AVMs can be further subdivided into glomerular (plexiforme or nidus-type) AVMs with a network of intervening vessels in between the artery and vein and the fistulous pial AVMs. The last step in the classification then describes whether the type of fistula has a high or a low shunting volume which will differentiate the “Macro-” from the “Micro-”fistulae. The proposed classification is therefore based on a stepwise analysis of the shunt including its arterial anatomy, its nidus-architecture and its flow-volume evaluation. The major advantage of this approach is that it leads to a subclassification with direct implications on the choice of treatment, thereby constituting a simple and practical approach to evaluate these rare diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".