The clinical study of 60 patients with subcortical ischemic vascular dementia
Bibliographic record
Abstract
Objective: To observe the clinical character of the patients with subcortical ischemic vascular dementia(SIVD).Methods: Collecting 60 SIVD patients and 45 matched nondemented aging. All patients and controls received history taking,detailed clinical examination and laboratory examination, including serum levels of homocysteine(Hcy), high sensitive Creactive protein(hs-CRP) and interleukin-6(IL-6). All participants received cognitive handicap assessments, such as minimental state examination(MMSE), Montreal cognitive assessment(MoCA) and clock drawing test(CDT). Counting the amount of lacunar infarcts, assess the severity of periventricular leukoaraiosis according age related white matter changes(ARWMC) on conventional brain MRI. Results:In SIVD, the clinical symptoms are gait disorder(31.7%), dysarthria(11.7%), water choking(8.3%), urinary incontinence(8.3%), and neurological signs are upper motor neuron damage signs(45.0%), ataxia(13.3%), pseudobulbar palsy(10.0%). Compared with the controls, the risks of SIVD are hypertension(P0.01), hypercholesterolemia and high serum levels of homocysteine(all P0.05). MMSE and MoCA of SIVD were significant lower than that of controls( P 0. 05). In SIVD group, the amount of lacunar infarct were significance greater then the controls( P0.01).Conclusions:(1)In SIVD, the clinical symptoms are gait disorder, dysarthria, water choking, urinary incontinence, and neurological signs are upper motor neuron damage signs, ataxia, pseudobulbar palsy. The important risks of SIVD are hypertension, hypercholesterolemia and high serum levels of homocysteine.(2) Besides MMSE and MoCA are sensitive for SIVD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".