[Cognitive function and factors related to cognitive function in hospitalized patients with chronic heart failure].
Bibliographic record
Abstract
OBJECTIVE: To explore the cognitive function status and potential influence factors on cognitive function in hospitalized patients with chronic heart failure. METHODS: Using a cross-sectional research design, CHF patients (n = 267, mean age (63.8 ± 9.4) years) were recruited from two care units-Heart Failure Care Unit and Intensive Care Unit of our hospital. Cognitive function status was evaluated by the Montreal Cognitive Assessment (MoCA) screen test. RESULTS: Based on the MoCA score, 37.8% (101/267) patients suffered from cognitive impairment (score<26), especially on the three specific cognitive functions-memory, langue and executive capability. Multifactorial analysis showed that after controlling for other factors, there was a downward trend on cognitive function with aging (OR = 1.09, 95%CI:1.05-1.14) while higher left ventricular ejection fraction (LVEF) was related to better cognitive function (OR = 0.97, 95%CI:0.95-0.99); patients who took medicine regularly also had better cognitive function (OR = 3.71, 95%CI:1.40-10.91); cognitive function was better in patients with high level of social support compared those with low social support (OR = 0.92, 95%CI:0.88-0.96). CONCLUSIONS: Incidence of cognitive impairment is high in hospitalized patients with chronic heart failure. Age, LVEF, whether taking medication regularly and social support are factors related to cognitive function in CHF patients.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".