Is there a difference in weight over time in heart failure patients based on cognitive function?
Bibliographic record
Abstract
Background and Objectives: Patients with heart failure (HF) have a high hospital readmission rate within 30 days of discharge due to dyspnea caused by water weight gain. Additionally, 22% of HF patients have cognitive impairment (CI) impacting their ability to engage in self-care activities. There is a paucity of research to measure the impact of cognitive function on change in weight over time. Design: This was an observational, longitudinal study of adult patients, with Class II-IV HF who were evaluated at time of discharge (T1) from a hospitalization for acute exacerbation of HF and at a clinic visit (T2) 4-8 weeks after discharge. The participants completed a 30-point Montreal Cognitive Assessment (MoCA) Inventory tool, and were weighed at T1 and T2. Results: Twenty-one participants (mean age 67.1 ± 12.2, 76.2% male, 81% Caucasian) completed both visits. The MoCA cutoff for mild CI was 24, where 42.9% of this sample scored below 24 at baseline. The change in weight between T1 and T2 was not statistically different (M change 0.43, SD 9.64; 95% CI, - 4.08 to 4.94; p = .84). The Independent T test of a change in weight in those HF participants with and without CI was not statistically different (high MoCA score, M change in weight = 0.62, SD 11.22; low MOCA scores, M change in weight = 0.20, SD 7.94; t(df 18) = - 0.09, p = .93). The re-hospitalization rate was 23.80% (n = 5). No cognitive assessment was documented in the medical record by healthcare professionals at time of hospital admission or at the clinic visit. Conclusion: Findings demonstrated there was no statistically significant difference in change in weight over time when participants were divided into low or high scores on the MoCA. The hospital readmission rate for HF was similar to that reported in other studies. This study found that assessments of cognitive function in HF are not being documented in the medical record. This study lays the groundwork for a larger study of the impact of CI on change in weight over time.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".