Predictive Validity of NYHA and ACC/AHA Classifications of Physical and Cognitive Functioning in Heart Failure
Bibliographic record
Abstract
In clinical and research settings, heart failure (HF) is typically assessed using the New York Heart Classification (NYHA class) criteria and/or the A merican College of Card iology/ American Heart Association (ACC/ AHA) Stages of HF criteria to assess severity and functional capacity of HF patients. Recent evidence suggests that there may be inconsistencies between the two classification schemes relative to correlations between physical function and cognitive function in HF patients. As cognitive function has been identified as a significant risk factor for patient outcomes, these inconsistencies between classification systems may limit the generalizability of the use of each assessment for research purposes. Therefore, the predict ive validity of these two classification schemas were examined for their association with physical ability measured by six-minute walk test (6MWT) and cognitive function screened using the Montreal Cognitive Assessment (Mo CA) in a published study of 90 patients with HF. The results revealed both the NYHA class (RS = -0.232, 0.028) and ACC/AHA stages of HF (RS = -0.258, p=0.014) have good predictive valid ity for determin ing cognitive indicating the need to include assessment of cognitive impairment as an at-risk factor among HF patients. The NYHA class revealed an inverse association with functional status measured by 6MWT (RS = -.298, p= 0.004), with no association to ACC/AHA stage of HF that indicates structural damage to the heart (RS = -.178; p= .093). Results support the impact of on functional status and cognitive function that are t wo important sequellae for considerations in staging severity and for patients with HF.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".