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Screening for Intracranial Stenosis With Transcranial Doppler: The Accuracy of Mean Flow Velocity Thresholds

2002· article· en· W2105927743 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Neuroimaging · 2002
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsUniversity of Calgary
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMedicineStenosisDigital subtraction angiographyTranscranial DopplerMiddle cerebral arteryPosterior cerebral arteryRadiologyMagnetic resonance angiographyStroke (engine)Internal carotid arteryBasilar arteryMagnetic resonance imagingCardiologyAngiographyConfidence intervalInternal medicineIschemia

Abstract

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BACKGROUND: Patients with 50% intracranial arterial stenosis may require more intensive therapies for stroke prevention. Transcranial Doppler (TCD) is a convenient noninvasive screen for intracranial stenosis. The accuracy of different mean flow velocity (MFV) thresholds for determining the degree of stenosis remains uncertain. METHODS: The authors prospectively compared the accuracy of TCD criteria and MFV thresholds to magnetic resonance, computed tomography, and digital subtraction angiography in patients with symptoms of recent or remote stroke or transient ischemic attack. Stenosis on angiography was measured as 0%, < 50%, or > or = 50% diameter reduction. RESULTS: Of 136 consecutive patients, 33 (24%) had distal internal carotid artery (ICA), middle cerebral artery (MCA), posterior cerebral artery, or basilar artery stenosis on angiography (14 patients [10%] were excluded due to incomplete TCD examinations, mainly from a lack of temporal windows). TCD showed 31 true-positive, 9 false-positive, 2 false-negative, and 94 true-negative studies. For all vessels, TCD had a sensitivity of 93.9% (confidence interval [CI] = 89%-98%), a specificity of 91.2% (CI = 87%-96%), a positive predictive value (PPV) of 77.5%, and a negative predictive value (NPV) of 97.9%. The trade-off in sensitivity and specificity for MCA MFV thresholds was as follows: MFV > or = 80 cm/s had a sensitivity of 100%, a specificity of 96.9% (CI = 94%-99%), a PPV of 84%, and an NPV of 100%. MFV > or = 100 cm/s had a sensitivity of 100%, a specificity of 97.9% (CI = 96%-99%), a PPV of 88.8%, and an NPV of 94.9%. MFV > or = 120 cm/s had a sensitivity of 68.7% (CI = 61%-78%), a specificity of 100%, a PPV of 100%, and an NPV of 94.9%. Reasons for false-positive findings include collateralization of flow in the presence of proximal ICA stenosis and prestenotic to stenotic MCA velocity ratios of 1: < or = 2. CONCLUSION: TCD is both sensitive and specific in identifying > or = 50% intracranial arterial stenosis. A MFV threshold cutoff of 100 cm/s has an optimal sensitivity and specificity trade-off for > or = 50% MCA stenosis. To help avoid false-positive results, a prestenotic to stenotic MCA velocity ratio of 1: > or = 2 should be used in addition to the MFV threshold.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.035
GPT teacher head0.262
Teacher spread0.227 · 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