Diabetes, Intracranial Stenosis and Microemboli in Asymptomatic Carotid Stenosis
Bibliographic record
Abstract
BACKGROUND: The risk of stroke in patients with asymptomatic carotid stenosis (ACS) is now so low that it is important to have methods to identify those patients most likely to benefit from intervention, or who may require special consideration in choice of medical therapy. We studied the prediction of stroke, death or transient ischemic attacks (stroke/death/TIA) in patients with ACS by intracranial arterial stenosis, and microemboli on transcranial Doppler (TCD), and the effect of diabetes mellitus on microemboli, intracranial stenosis and risk of events. METHODS: Patients with ACS > 60% by Doppler ultrasound were recruited from the Stroke Prevention Clinic of University Hospital, London, Canada. All 339 participants underwent TCD for detection of intracranial stenosis and detection of microemboli, and carotid ultrasound to measure extracranial stenosis and total carotid plaque area. Participants were followed for three years, to determine the risk of stroke/death/TIA. RESULTS: Stroke/death/TIA occurred in 38% of patients with microemboli versus 10% without (p=0.0001), and in 18% of patients with intracranial stenosis, versus 10% without (p=0.042). Diabetics were significantly more likely to have intracranial stenosis (45% vs. 29%, p =0.014), microemboli (38% vs. 10%, p <0.0001), and had significantly higher risk of stroke/death/TIA over three years (21% vs. 11% without; p=0.024). Survival free of stroke, TIA or death was significantly better without microemboli or intracranial stenosis (p<0.0001). CONCLUSIONS: Diabetes, microemboli and intracranial stenosis predicted higher risk of stroke, death or TIA than did extracranial stenosis or total plaque area; diabetics may need more intensive therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".