Is B-mode ultrasound alone a sufficient screening tool for carotid stenosis? A pilot study
Bibliographic record
Abstract
BACKGROUND: Carotid ultrasound is performed solely in hospital ultrasound departments or outpatient labs, using both B- and Doppler modes. We hypothesize that B-mode without Doppler can be used to classify patients as having carotid stenosis (CS) above or below 50%. Our objective is to determine the frequency with which a CS >50% is found using Doppler when no such stenosis was visible using B-mode. METHODS: This was a retrospective study of 100 patients referred to the stroke clinic and 100 patients referred for carotid endarterectomy (CEA). All patients had an elective carotid ultrasound done at Health Sciences North. The ultrasound reports were mixed together and blinded. Investigators determined if there was a CS of greater or less than 50% based on the carotid diagram. These results were compared to the degree of CS found on Doppler. RESULTS: In the CEA group, there were 198 ultrasounds, with 153 showing a CS of >50%. Only one case of CS >50% was missed by B-mode. In the clinic group, 32 of 192 ultrasounds showed a CS of >50%. None were missed by B-mode. B-mode had a sensitivity and negative predictive value of 100% and a specificity of 65%. CONCLUSION: This study supports the theory that it may be possible to use B-mode ultrasound without Doppler to reliably determine if there is CS above or below 50%. Further research is required before carotid ultrasound using B-mode alone can be recommended.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".