Dynamic Analysis of TCM Syndrome Elements Based on the Neuropsychological Characteristics of Cognitive Function Impairment after Ischemic Stroke
Bibliographic record
Abstract
Objective To analyze dynamic features of TCM syndrome elements according to the neuropsychological characteristics of cognitive function impairment after ischemic stroke.Methods The hospitalized cerebral infarction patients without dementia were evaluated dynamically with the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA) (Beijing Version) after IQCODE screens at different phase points.And they were divided into groups according to the severity of cognitive function impairment.The occurring frequency of TCM syndrome elements at all phase points for diagnosis was compared.Results The phase points and TCM syndrome elements closely related with the cognitive function impairment after ischemic stroke were as following:the 3rd day with fire and yin deficiency,the 7th day with yin deficiency,the 1st month with fire,phlegm,and blood stasis,and the 3rd month with fire,phlegm,blood stasis,qi deficiency,and yin deficiency.Conclusion The neuropsychological characteristics of cognitive function impairment of ischemic stroke are closely related with TCM syndrome elements and their evolvement as well as their different combinations.
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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 | 0.000 |
| 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".