The use of 4D-CTA in the diagnostic work-up of brain arteriovenous malformations
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate the use of time-resolved whole-head CT angiography (4D-CTA) in patients with an untreated arteriovenous malformation of the brain (bAVM), as demonstrated by catheter angiography (DSA). METHODS: Seventeen patients with a DSA-proven bAVM were enrolled. These were subjected to 4D-CTA imaging using a 320 detector row CT scanner. Using a standardized scoring sheet, all studies were analyzed by a panel of three readers. This panel was blind to the DSA results at the time of reading the 4D-CTA. RESULTS: 4D-CTA detected all bAVMs. With regard to the Spetzler-Martin grade, 4D-CTA disagreed with DSA in only one case, where deep venous drainage was missed. Further discrepancies between 4D-CTA and DSA analyses included underestimation of the nidus size in small lesions (four cases), misinterpretation of a feeding vessel (one case), misinterpretation of indirect feeding through pial collaterals (three cases) and oversight of mild arterial enlargement (two cases). 4D-CTA correctly distinguished low-flow from high-flow lesions and detected dural/transosseous feeding (one case), venous narrowing (one case) and venous pouches (nine cases). CONCLUSION: In this series, 4D-CTA was able to detect all bAVMs. Although some angioarchitectural details were missed or misinterpreted when compared to DSA, 4D-CTA evaluation was sufficiently accurate to diagnose the shunt and classify it. Moreover, 4D-CTA adds cross-sectional imaging and perfusion maps, helpful in treatment planning. 4D-CTA appears to be a valuable new adjunct in the non-invasive diagnostic work-up of bAVMs and their follow-up when managed conservatively.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".