Association between the extent of white matter damage and early cognitive impairment following acute ischemic stroke
Bibliographic record
Abstract
White matter (WM) injury following acute ischemic stroke (AIS) is associated with cognitive decline. Establishing relationships between the specific cognitive tests used to assess post-AIS cognition and various clinical indices of WM injury severity and distribution may aid in prognosis and early treatment decisions. We enrolled 62 patients with AIS to Weifang People's Hospital between September 2014 and August 2015. WM lesion severity and distribution were examined by computed tomography (CT) and magnetic resonance imaging (MRI). The Blennow scale was used for scoring the distribution and degree of WM lesions (WMLs) on CT images, the Fazekas scale for scoring periventricular and deep WMLs on MRI, and the Cholinergic Pathways Hyperintensities Scale (CHIPS) for scoring MRI manifestation of cholinergic fiber damage. The 8-domain Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive function. Mean ± standard deviation scores on the Blennow scale was 1.6±0.5; Fazekas scale, 3.4±0.8; and CHIPS, 65.7±12.5. The proportion of patients with a MoCA score <26 (indicating cognitive dysfunction) was significantly higher in subgroups with Blennow scale score >2, Fazekas scale score >4, and CHIPS score >51 (all P<0.001). The MoCA score was negatively correlated with Blennow scale score (r=-0.326, P=0.002), Fazekas scale score (r=-0.404, P=0.031), and CHIPS score (r=-0.234, P=0.042). Thus, the degree and distribution of whole-brain, deep, and cholinergic WMLs were associated with cognitive impairment. The Blennow scale, Fazekas scale, and CHIPS all provide good predictive efficacy of post-AIS cognitive dysfunction.
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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 | 0.001 | 0.001 |
| 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".