White Matter Hyperintensities According to Neuroimaging Analysis, Cognitive Impairment and Emotional Disorders: Is There a Link?
Bibliographic record
Abstract
Background. Cognitive decline is one of leading contributors to the loss of independence in older adults. Therefore, early diagnosis and detection of potentially modifiable cognitive disorders is a significant challenge for modern geriatrics. Aim. To assess the relationship between cognitive impairment and presence of leukoareosis through neuroimaging in older adults. Materials and methods. General population cohort study of 102 patients aged 60–98 years treated at The St. Petersburg Hospital for War Veterans between September and December 2019. Cognitive assessment (The Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE)), depression (The Geriatric Depression Scale), sleep complaints, subjective cognitive decline, computed tomography (CT) scan. Results. The studied patients were divided into two groups: with the presence of leukoareosis (n=59) and without leukoareosis (n=43). Patients with leukoareosis had significantly lower total MoCA scores. They performed significantly worse in domains of visual–structural skills and attention. As for MMSE, patients with leukoareosis also performed significantly worse in repeating a sentence and descending subtraction task. There was no statistically significant difference in GDS scores between the two groups. However, patients with leukoareosis significantly more frequently considered their lives less fulfilling and their memory worse. They also abandoned most of their former interests. Conclusion. If leukoareosis is detected on CT scans, it is necessary to evaluate cognitive functions; the presence of leukoareosis in patients was associated with an increased risk of cognitive disorders and depression.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".