Abstract 13037: Self-Care Questionnaire Detects More Brain Injury in Comparison to Cognitive Screener in Heart Failure
Bibliographic record
Abstract
Introduction: Heart failure (HF) patients often have poor self-care (SC) and cognitive deficits. Both factors are closely related and require brain control of important skill sets. However, it is unclear if SC or cognition are linked to injury in different brain regions or if SC vs. cognition evaluation differs in the detection of brain damage amounts. Hypothesis: To determine if a measure of SC or cognition detects more brain injury in HF. Methods: Brain diffusion Tensor Imaging (DTI) data were collected from 10 HF (age 56.8±8.1 years; 8 male; LVEF 26.4±11.9% NYHA class II/III) using a 3 Tesla MRI scanner, Self-care was examined with the Self-Care of Heart Failure Inventory (SCHFI), and cognition with the Montreal Cognitive Assessment (MoCA) test. Using DTI data, mean diffusivity (MD; higher values=brain injury) maps were used to examine associations between SCHFI (maintenance and confidence subscales) and MoCA in HF subjects using partial correlation procedures (covariates: age and gender; uncorrected threshold p<0.005). Results: Significant negative correlations between MD and confidence scores emerged in the prefrontal cortex. hippocampus, amygdala, anterior and posterior cingulate, anterior corpus callosum, mid and posterior thalamus, posterior insula, putamen, inferior temporal lobe, and cerebral vermis, and between MD and maintenance scores in the right putamen, external and internal capsule, and right inferior temporal lobe (Figure). Significant negative correlations were observed between MD and MoCA scores in the prefrontal cortex, occipital cortex, cerebellum, and mid-inferior temporal lobe (Figure). Conclusions: HF subjects show more wide-spread and significant correlations between regional MD values/brain injury and SCHFI scores compared to MoCA values. The findings suggest that damage in cognitive and decision-making control sites better correlate with SCHFI over MoCA scores.
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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 | 0.000 |
| 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) | 0.005 | 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".