Abstract 16628: Cognitive Dysfunction, Instrumental Activities of Daily Living, and Health-related Quality of Life in Heart Failure
Bibliographic record
Abstract
Introduction: Cognitive dysfunction is common in heart failure (HF), but relationships are unclear among cognitive dysfunction, performance-based instrumental activities of daily living (IADL), and health-related quality life (HRQL). Objectives: Guided by HRQL and IADL theories, hypotheses were: 1) worse cognitive function in global cognition, memory, executive function is associated with worse performance in IADL; 2) worse performance in IADL is associated with worse HRQL; and 3) IADL mediates the relationship between cognitive dysfunction and HRQL. Methods: In a cross-sectional analysis, baseline data were used from a randomized controlled trial of cognitive training in HF (N=256, mean age 66; 54% women; EF 49%; NYHA Class I=9%, II=36%, III=55%). Measures were: global cognition-Montreal Cognitive Assessment; memory-Hopkins Verbal Learning Test; executive function-Category fluency; IADL-Everyday Problem Test; and HRQL-Living with Heart Failure Questionnaire. Higher scores of cognitive and IADL measures indicate better performance. Higher scores of HRQL indicate worse HRQL. Covariates were age, gender, education, NYHA, and depressive symptoms. Multiple linear regressions were used to test hypotheses. Results: Hypothesis 1 was supported. Worse cognitive function in global cognition, memory, executive function was associated with worse IADL (β=0.38~0.45, p<.001, R 2 =0.19~0.22). Age and education were significant covariates. Hypothesis 2 was supported. Worse IADL was associated with worse HRQL (β =0.79, p <.001, R 2 =0.55). Gender, NYHA Class, and depressive symptoms were significant covariates. Hypothesis 3 was not tested because cognitive function was not significantly related to HRQL. Conclusions: Cognitive dysfunction was associated with worse performance in IADL. Worse performance in IADL was associated with worse HRQL. Interventions to improve HRQL may need to target improving performances in both cognitive function and IADL.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".