Bedside Emergency Transcranial Doppler Diagnosis of Severe Carotid Disease Using Orbital Window Examination
Bibliographic record
Abstract
Identifying internal carotid artery (ICA) stenosis in the acute stroke setting can provide clinically useful information. Transcranial Doppler (TCD) through the orbital window is an easy test to perform and to track and identify different vessels. Previous TCD studies have suggested that a reversed ophthalmic artery (OA) flow is a useful collateral pattern to predict ICA disease. The authors sought to evaluate the TCD orbital window for predicting cervical ICA (cICA) stenosis in the setting of acute stroke and TIA.Power M-mode/TCD was performed in acute stroke and transient ischemic attack patients at 2 institutions. Each orbital window depth was detected on M-mode and evaluated for the direction of flow and resistance pattern. Gold standard for comparison was carotid evaluation using carotid duplex, computed tomography angiogram, or conventional angiography. The assessment of cICA disease was categorized by degree of stenosis or occlusion.A total of 216 transorbital exams were performed in 117 patients. Twenty-five cICA occlusions and 8 critical cICA stenoses (>or=95%) were identified by gold standard imaging. Reversed OA flow at 50 to 60 mm depth revealed high specificity (100%; confidence interval [CI], 97.6%-100.0%) and good sensitivity (75%; CI, 53.3%-90.2%) for identifying cICA occlusion or critical stenosis (>or=95%). Low pulsatility index (<1.2) and mean flow velocity (<15 cm/s) discriminated critical severe ICA stenosis or occlusion when OA flow was anterograde with good sensitivity (87.2%) and specificity (95.2%).The reversed OA sign at 50 to 60 mm depth is very specific for identifying cICA occlusion or critical stenosis. When OA flow is anterograde, a low mean flow velocity or pulsatility index is also useful to identify cICA critical stenosis or occlusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".