Effect of -deficiency constitution on cognitive impairment in cerebral small vessel disease and its neuroimaging mechanism.
Bibliographic record
Abstract
OBJECTIVE: To explore the effect of Traditional Chinese Medicine (TCM) constitution on cognitive impairment (CI) in patients with cerebral small vessel disease (CSVD) and its underlying neuroimaging mechanism and to provide countermeasures for health management of CSVD patients. METHODS: A total of 241 CSVD patients were recruited from the Department of Neurology. All subjects underwent head magnetic resonance imaging (MRI), Chinese Medicine Questionnaire (CCMQ) and cognitive function examination. The CSVD patients were divided according to the Montreal Cognitive Assessment (MoCA) score into a normal cognitive group (73 cases) and a CI group (168 cases). Logistic regression was used to analyse the risk constitution of CSVD-CI and to construct a risk prediction model. 3DT1 MRI images and FreeSurfer 6.0 software (Athinoula A. Martinos, Boston, MA, USA) were used to further explore the involvement of hippocampal subregion volume in patients with at-risk constitution and its correlation with cognitive function. RESULTS: 0.05). CONCLUSION: CSVD patients with YADC are more prone towards developing CI, and asymmetric atrophy of the hippocampus might be the underlying neuroimaging mechanism. In particular, the volume of the left whole hippocampus and the left hippocampal molecular layer correlated significantly with general cognitive function.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".