Magnetic Resonance (MR) Imaging of Vascular Malformations
Bibliographic record
Abstract
Vascular malformations pose a diagnostic and therapeutic challenge due to the broad differential diagnosis as well as common utilization of inadequate or inaccurate classification systems among healthcare providers. Therapeutic approaches to these lesions vary based on the type, size, and extent of the vascular anomaly, necessitating accurate diagnosis and classification. Magnetic resonance (MR) imaging (MRI) is an effective modality for classifying vascular anomalies due to its ability to delineate the extent and anatomic relationship of the malformation to adjacent structures. In addition to anatomical mapping, the complete evaluation of vascular anomalies includes hemodynamic characterization. Dynamic time-resolved contrast-enhanced MR angiography provides information regarding hemodynamics of vascular anomalies, differentiating high- and low-flow vascular malformations. Radiologists must identify the MRI features of vascular malformations for better diagnosis and classification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".