Paediatric cerebrovascular syndromes: imaging clues to a diagnosis
Bibliographic record
Abstract
Central nervous system (CNS) vascular syndromes are being increasingly recognized and diagnosed in the paediatric population. Their rarity and complexity makes diagnosis challenging. These syndromes can present in a number of different ways clinically and radiologically. A systematic method of image interpretation often reveals a pattern, allowing the diagnosis to be narrowed or further testing to be directed. Although the CNS vascular appearance itself is rarely specific, additional information gleaned from imaging other systems aids in pattern recognition. We retrospectively reviewed paediatric cases of CNS vasculopathy with a confirmed diagnosis of a syndromic disorder. We recorded the predominant CNS vascular appearance in each case, and assessed imaging clues from multiple systems, including brain, orbits, skull, spine, long bones, viscera, and skin. Several of these syndromes also had systemic vascular involvement, which allowed further categorization. Some imaging patterns allowed a specific diagnosis to be made. In other cases, the constellations of imaging appearances helped distinguish between clinically similar phenotypes. The imaging pattern within each paediatric CNS vascular syndrome is fairly consistent, and awareness of these syndromes and expected imaging patterns allow the radiologist to suggest further imaging or targeted genetic testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".