Cerebral hemodynamic severity of asymptomatic carotid artery stenosis/occlusion estimated by neurocognitive domains
Bibliographic record
Abstract
Background The severity of cerebral hemodynamic impairment in patients with carotid artery stenosis or occlusion (CASO) is not always correlated with the severity of stenosis or occlusion. There are no established noninvasive indicators of cerebral hemodynamic impairment in CASO. Objective The study aimed to identify impaired neurocognitive domains as promising noninvasive severity markers. Methods In a retrospective study of 142 patients with asymptomatic CASO, we assessed associations of Montreal Cognitive Assessment (MoCA) and Alzheimer's Disease Assessment Scale-Cognitive Subscale 14 (ADAS-Cog) with 15 O-gas positron emission tomography. Results In patients with right CASO (n = 45), worse delayed recall on MoCA was significantly associated with decreased CBF (adjusted β for cerebral blood flow [CBF] 1.70, 95% confidence interval [CI] 0.21–3.28, p = 0.027), with the cutoff value of 1/2 for CBF of <35.0 mL/100 g/min (sensitivity 100%). In patients with left CASO (n = 60), language comprehension on ADAS-Cog was significantly associated with increased OEF (adjusted β for oxygen extraction fraction 5.48, 95% CI 2.29–8.67, p = 0.001), with the cutoff value of 0/1 for OEF of >52.9% (specificity 96.4%). In patients with bilateral CASO (n = 37), worse executive function on MoCA score was significantly associated with decreased CBF (adjusted β for CBF 2.70, 95% CI 0.29–5.13, p = 0.030), with the cutoff value of 2/3 for CBF of <35.0 mL/100 g/min (sensitivity 83.3%). Conclusions Neuropsychological examinations could be useful for noninvasively estimating the severity of cerebral hemodynamic impairment in patients with CASO.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".