Cognitive impairment and gray matter volume abnormalities in silent cerebral infarction
Bibliographic record
Abstract
To investigate the association between cognitive impairment and gray matter volume (GMV) abnormalities in silent cerebral infarction (SCI) patients, the GMV of 62 pairs of patients and well-matched healthy controls was calculated. All participants underwent a P300 test, a Montreal Cognitive Assessment (MoCA) test. Compared with controls, the patients showed decreased GMV in the left superior frontal gyrus, left inferior frontal gyrus, left superior temporal gyrus, right middle temporal gyrus, and bilateral parahippocampal gyrus; no significantly increasing GMV was found. The volumes of the frontal and temporal lobes were positively correlated with the score of the MoCA scale and P300 amplitudes (r≥0.62, P<0.01). The P300 latency was negatively correlated with the volumes of the frontal lobe, the temporal lobe, and the hippocampus (r≤-0.71, P<0.05). No significant correlations between the GMV of the abnormal brain regions and four clinical characteristics in SCI patients were found, suggesting that cognitive deficiency existed in SCI patients and the reduced GMV might contribute to the pathology of cognitive deficiency in SCI patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".