Abstract WMP47: Traditional Doppler Measures Do Not predict Cognition in a Cohort With Advanced Atherosclerosis
Bibliographic record
Abstract
Introduction: Traditional Doppler measures have been used to predict cognitive performance in patients with carotid atherosclerosis. Novel measures, such as carotid strain indices (CSI) have shown associations with cognitive performance. We hypothesized that lower mean middle cerebral artery (MCA) velocities, higher bulb-internal carotid artery (ICA) velocities, MCA pulsatility index (PI) and CSI would be associated with poorer cognitive performance in individuals with advanced atherosclerosis. Methods: Neurocognitive testing, carotid ultrasound, transcranial Doppler and CSI were performed on 40 subjects scheduled for carotid endarterectomy. Kendall tau correlations were used to examine relationships between cognitive tests and the maximum peak systolic velocity (PSV) (from bulb, proximal, mid or distal ICA), mean MCA velocity and PI (on surgical side) and CSI (maximum axial, lateral and shear strain indices used to characterize plaque deformations with arterial pulsation). Cognitive measures included age adjusted indices of verbal fluency, verbal and visual learning/memory, psychomotor speed, auditory attention/working memory, visuoconstruction, and mental flexibility. Results: Participants were median age 71.0 (IQR 9.75) years, 26 male (65%) and 14 female (35%). Median stenosis was 70.00 (IQR 10.00) percent. Traditional Doppler parameters, PSV, mean MCA velocity and MCA PI did not predict cognitive performance (p values all >0.05). Maximum strain values were significantly associated with cognitive performance (p<0.05). Table . Conclusions: Traditional velocity measurements of maximum bulb-ICA PSV, mean MCA velocity and PI were not associated with cognitive performance in patients with advanced atherosclerotic disease, however maximum strain indices were associated with cognitive performance. Findings suggest that cognition may be associated with unstable plaque (plaques at greater risk for rupture) rather than blood flow.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".