Benefit of Echocontrast-Enhanced Transcranial Arterial Color-Coded Duplex Ultrasound
Bibliographic record
Abstract
OBJECTIVES: Proper assessment of the intracranial arteries by transcranial color-coded duplex sonography (TCCD) is occasionally made difficult by an insufficient temporal bone window, an unfavorable insonation angle, or low flow velocity or volume. In these cases, echocontrast could be helpful to increase the diagnostic confidence or to make the diagnosis at all. MATERIAL AND METHODS: We investigated 67 temporal windows of 47 patients with insufficient native transtemporal insonation conditions before and after the application of the second-generation (gas-filled) microbubble contrast agent Sonovue (in 20 patients out of these 47, both temporal windows were insufficient, in the remaining 27 only one side). RESULTS: As compared to the precontrast scans, echocontrast allowed for more segments to be evaluated by pulsed Doppler sonography (p < 0.0001) and for longer lumen segments to be displayed on color mode (p < 0.0001). With the help of contrast medium, flow velocity in the middle cerebral artery could be measured through 65 windows as compared to only 26 windows before contrast was applied (p < 0.0001). CONCLUSIONS: In patients with poor precontrast visualization of intracranial arteries, echocontrast-enhanced TCCD is very helpful.
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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 | 0.000 | 0.006 |
| 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) | 0.002 | 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".