Cerebral vascular malformations: Time-resolved CT angiography compared to DSA
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to prospectively test the hypothesis that time-resolved CT angiography (TRCTA) on a Toshiba 320-slice CT scanner enables the same characterization of cerebral vascular malformation (CVM) including arteriovenous malformation (AVM), dural arteriovenous fistula (DAVF), pial arteriovenous fistula (PAVF) and developmental venous anomaly (DVA) compared to digital subtraction angiography (DSA). MATERIALS AND METHODS: Eighteen (eight males, 10 females) consecutive patients (11 AVM, four DAVF, one PAVF, and two DVA) underwent 19 TRCTA (Aquillion one, Toshiba) for suspected CVM diagnosed on routine CT or MRI. One patient with a dural AVF underwent TRCTA and DSA twice before and after treatment. Of the 18 patients, 13 were followed with DSA (Artis, Siemens) within two months of TRCTA. Twenty-three sequential volume acquisitions of the whole head were acquired after injection of 50 ml contrast at the rate of 4 ml/sec. Two patients with DVA did not undergo DSA. Two TRCTA were not assessed because of technical problems.TRCTAs were independently reviewed by two neuroradiologists and DSA by two other neuroradiologists and graded according to the Spetzler-Martin classification, Borden classification, overall diagnostic quality, and level of confidence. Weighted kappa coefficients (k) were calculated to compare reader's assessment of DSA vs TRCTA. RESULTS: There was excellent (k = 0.83 and 1) to good (k = 0.56, 0.61, 0.65 and 0.67) agreement between the different possible pairs of neuroradiologists for the assessment of vascular malformations. CONCLUSION: TRCTA may be a sufficient noninvasive substitute for conventional DSA in certain clinical situations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".