Near-occlusion is difficult to diagnose with common carotid ultrasound methods
Bibliographic record
Abstract
PURPOSE: To assess the sensitivity and specificity of common carotid ultrasound method for carotid near-occlusion diagnosis. METHODS: Five hundred forty-eight patients examined with both ultrasound and CTA within 30 days of each other were analyzed. CTA graded by near-occlusion experts was used as reference standard. Low flow velocity, unusual findings, and commonly used flow velocity parameters were analyzed. RESULTS: One hundred three near-occlusions, 272 conventional ≥50% stenosis, 162 <50% stenosis, and 11 occlusions were included. Carotid ultrasound was 22% (95%CI 14-30%; 23/103) sensitive and 99% (95%CI 99-100%; 442/445) specific for near-occlusion diagnosis. Near-occlusions overlooked on ultrasound were found misdiagnosed as occlusions (n = 13, 13%), conventional ≥50% stenosis (n = 65, 63%) and < 50% stenosis (n = 2, 2%). No velocity parameter or combination of parameters could identify the 65 near-occlusions mistaken for conventional ≥50% stenoses with >75% sensitivity and specificity. CONCLUSION: Near-occlusion is difficult to diagnose with commonly used carotid ultrasound methods. Improved carotid ultrasound methods are needed if ultrasound is to retain its position as sole preoperative modality.
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 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) | 0.001 | 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) | 0.001 | 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 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".