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Record W2052869360 · doi:10.1159/000087933

Benefit of Echocontrast-Enhanced Transcranial Arterial Color-Coded Duplex Ultrasound

2005· article· en· W2052869360 on OpenAlexfundno aff
Dirk W. Droste, Thomas Boehm, Martin Ritter, Ralf Dittrich, E B Ringelstein

Bibliographic record

VenueCerebrovascular Diseases · 2005
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsnot available
FundersAtlantic Canada Opportunities Agency
KeywordsMedicineRadiologyLumen (anatomy)Transcranial DopplerTemporal boneMiddle cerebral arteryCerebral arteriesContrast (vision)UltrasoundSurgeryCardiologyIschemia

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.006
GPT teacher head0.225
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2005
Admission routes1
Has abstractyes

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