Extent of spontaneous cross-flow via the anterior communicating artery in steno-occlusive carotid artery disease
Bibliographic record
Abstract
PURPOSE: Our purpose was to evaluate the agreement of transcranial color-coded duplex sonography (TCCS) measurements and intra-arterial digital subtraction angiography (DSA) findings in determining the extent of spontaneous cross-flow via the anterior communicating artery (AcoA) in patients with internal carotid artery (ICA) stenosis. METHODS: Thirty adult patients with suspected uni- or bilateral high-grade carotid artery stenosis were prospectively examined by DSA and angle-corrected TCCS. The extent of cross-flow was determined considering retrograde flow in the ipsilateral anterior cerebral artery (ACA) and sideto-side differences of the A1-segments of the ACA and middle cerebral arteries (MCAs) by both techniques. Cross-flow was angiographically categorized by means of a four-step scale. DSA findings were correlated with side-to-side differences in mean blood flow velocity as well as flow direction measured by TCCS. RESULTS: Twenty-seven of 30 patients had a uni- or bilateral ICA stenosis of >49%. Excellent agreement between TCCS and DSA was evaluated for the detection of lack (grades 0 and 1) or presence (grades 2 and 3) of reversed flow in the ACA (sensitivity 100%, specificity 93%, positive predictive value 94%). Post hoc analysis of the mean velocities in the ACA and MCA revealed a side-to-side difference of 25 cm/s as a cutting point allowing for definition of a corresponding four-grade scale for ultrasound. However, full agreement, i.e.same grade of cross-flow detected by both techniques, was only found in 17(57%) of 30 cases. CONCLUSIONS: Non-invasive TCCS is reliable for detecting reversed flow in the ACA in patients with ICA stenosis. However, there is only a moderate agreement between angiography and TCCS in quantifying the extent of spontaneous anterior cross-flow because different information on the intracranial hemodynamics may be obtained.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".