The use of 4D-CTA in the diagnostic work-up of brain arteriovenous malformations
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it