La maladie des petits vaisseaux cardiaques et cérébraux chez des patients atteints d’insuffisance cardiaque à fraction d’éjection préservée : une étude prospective
Bibliographic record
Abstract
Heart failure with preserved ejection fraction (HFpEF), increasingly prevalent with aging and metabolic comorbidities, remains poorly understood, particularly in relation to cognitive impairment. Small vessel disease, whether involving the heart or the brain, constitutes an explanatory pathway : myocardial microcirculatory dysfunction and cognitive impairment related to silent cerebral lesions. The present work investigated the association between microvascular impairment and cognitive function in patients with HFpEF, compared with a control group. In this prospective observational study (Bordeaux university hospital, 2023-2025), 68 patients were included, 37 with HFpEF. The evaluation included cognitive assessment using the Montreal cognitive assessment (MoCA), brain magnetic resonance imaging (MRI) with calculation of the Total SVD score, and, in the HFpEF subgroup, invasive assessment of coronary microcirculation. Compared with controls, patients with HFpEF were older, frequently obese, and predominantly women. Cognitive performance was similar, as was the Fazekas score. The total small vessel disease (SVD) score was not significantly different, although it tended to be higher in the control population. From a cardiac perspective, invasive exploration revealed microcirculatory dysfunction in a subset of patients, confirming both the feasibility and the relevance of this approach. This work highlights the importance of an integrated approach to HFpEF within the framework of small vessel disease, where parallel investigation of the heart and the brain may help refine the phenotypic characterization of this complex condition and pave the way for more targeted therapeutic strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".