Left atrial fibrosis: an essential hallmark in chronic mitral regurgitation
Bibliographic record
Abstract
Abstract Chronic mitral regurgitation (MR) is the second valvular heart disease for incidence, which worsening severity gradually affects all cardiac chambers and leads to poor outcome if untreated. The recent development of minimally invasive surgical techniques and percutaneous intervention has reduced the operative risk, allowing a more confident referral of these patients for intervention. Therefore, there is a growing need of reliable markers to select the best therapeutic strategies and to identify the optimal timing for intervention. Myocardial fibrosis (MF) gradually occurs as a result of left atrial and ventricular (LA and LV) remodeling due to MR pressure and volume overload. It has been identified as an index of clinical outcome and arrhythmic risk in patients with MR. Particularly, the assessment of LA fibrosis not only allows to define different MR etiology, but also was associated with prognosis and atrial fibrillation (AF) burden. Nowadays, noninvasive estimation of MF is possible through the use of advanced imaging modalities, particularly cardiac magnetic resonance and speckle tracking echocardiography. This review discusses the role of LA fibrosis as a diagnostic and prognostic marker in patients with MR and its quantification by noninvasive multimodality cardiac imaging.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".