Cognitive Test Results are Associated with Mortality and Rehospitalization in Heart Failure: Swedish Prospective Cohort Study
Bibliographic record
Abstract
AIMS: We aimed to search for associations between cognitive test results with mortality and rehospitalization in a Swedish prospective heart failure (HF) patient cohort. METHODS AND RESULTS: Two hundred and eighty-one patients hospitalized for HF (mean age, 74 years; 32% women) were assessed using cognitive tests: Montreal Cognitive Assessment (MoCA), A Quick Test of Cognitive speed, Trail Making Test A, and Symbol Digit Modalities Test. The mean follow-up time censored at rehospitalization or death was 13 months (interquartile range, 14) and 28 months (interquartile range, 29), respectively. Relations between cognitive test results, mortality, and rehospitalization risk were analysed using multivariable Cox regression model adjusted for age, sex, body mass index, systolic blood pressure, atrial fibrillation, diabetes, smoking, educational level, New York Heart Association class, and prior cardiovascular disease. A total of 80 patients (29%) had signs of cognitive impairment (MoCA score < 23 points). In the fully adjusted Cox regression model using standardized values per 1 SD change of each cognitive test, lower score on MoCA [hazard ratio (HR), 0.75; confidence interval (CI), 0.60-0.95; P = 0.016] and Symbol Digit Modalities Test (HR, 0.66; CI, 0.48-0.90; P = 0.008) yielded significant associations with increased mortality. Rehospitalization risk (n = 173; 62%) was significantly associated with lower MoCA score (HR, 0.84; CI, 0.71-0.99; P = 0.033). CONCLUSIONS: Two included cognitive tests were associated with mortality in hospitalized HF patients, independently of traditional risk factors. In addition, worse cognitive test scores on MoCA heralded increased risk of rehospitalization.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".