Impact and Implications of Neurocognitive Dysfunction in the Management of Ischemic Heart Failure
Bibliographic record
Abstract
Neurocognitive dysfunction is common in heart failure (HF), with 30% to 80% of patients experiencing some degree of deficits in one or more cognitive domains, including memory, attention, learning ability, executive function, and psychomotor speed. Although the mechanism is not fully understood, reduced cardiac output, comorbidities, chronic cerebral hypoperfusion, and cardioembolic brain injury leading to cerebral hypoxia and brain damage seem to trigger the neurocognitive dysfunction in HF. Cognitive impairment is independently associated with worse outcomes including mortality, rehospitalization, and reduced quality of life. Patients with poorer cognitive function are at an increased risk of severe disease as they tend to have greater difficulty complying with treatment requirements. Coronary revascularization in patients with ischemic HF has the potential to improve cardiovascular outcomes but risks worsening neurocognitive dysfunction even further. Revascularization by coronary artery bypass grafting carries inherent risks for delirium, cognitive impairment, neurologic injury, and stroke, which are known to exacerbate the risk of neurocognitive dysfunction. Alternatively, percutaneous coronary intervention, as a less-invasive approach, has the potential to minimize the risk of cognitive impairment but has not yet been evaluated as an alternative to coronary artery bypass grafting in patients with ischemic HF. Therefore, it is paramount to raise awareness of the neurocognitive consequences in ischemic HF and devise strategies for recognition and prevention as an important target of patient management and personalized decision making that contributes to patient outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".