Abstract 4140382: Does pain predict physical function, cognitive function, activities of daily living, or health-related quality life among adults with heart failure?
Bibliographic record
Abstract
Introduction: Although an estimated 54% of patients with heart failure (HF) and chronic pain report high symptom-associated distress, it is unclear whether pain predicts reduced physical function, cognitive function, independent activities of daily living (IADL) or health-related quality of life (HRQL) over time. The aims were to evaluate baseline pain presence as a predictor of physical function, cognitive function, IADL, and HRQL at baseline, 10 weeks, 4 months, and 8 months after baseline. Methods: In a retrospective longitudinal secondary analysis, data were analyzed from 237 participants with HF enrolled in the Cognitive Intervention to Improve Memory in Heart Failure Patients study. Pain presence was measured with the Health Utilities Index Mark-3 Questionnaire (HUI-3), physical function was measured by the Timed Up and Go (TUG), cognitive function was measured with the Montreal Cognitive Assessment (MoCA), IADL was measured by the Everyday Problems Test (EPT), and HRQL was measured by the Minnesota Living with Heart Failure Questionnaire (LHFQ). Descriptive statistics, independent t-tests, and linear mixed models were used to achieve the aims while controlling for gender. Results: The demographics were mean age 66.31 ± 12.02 years, gender 46% men, 54% women, race 13.5% Black, 85.7% White, 0.8% Other, NYHA class I: 9.7% II: 37.6% III: 52.7%, average LVEF: 48.9%. A total of 160 (67.51%) reported pain. In independent t-tests, patients with pain experienced significantly longer (i.e., worse) TUG scores at all timepoints except 10 weeks, and significantly higher (i.e., worse) LHFQ scores at all timepoints (see Table 1). However, in linear mixed models, pain at baseline did not predict TUG scores (F = 1.239, p = .298), MoCA scores (F = 0.148, p = .931), EPT scores, (F = 0.522, p = .668), or LHFQ scores (F = 0.364, p = .779) over time – see Table 1. Conclusions: Patients with HF and pain experienced significantly worse LHFQ and TUG scores at multiple timepoints. However, pain did not significantly predict cognitive function, physical function, IADL, or HRQL over time. Future prospective studies are needed to examine other outcomes associated with pain in this population and utilize more robust pain instruments.
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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 | 0.002 | 0.003 |
| 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) | 0.003 | 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".