Echocardiographic parameters, speckle tracking, and brain natriuretic peptide levels as indicators of progression of indeterminate stage to Chagas cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Chronic Chagas cardiomyopathy (CCM) is characterized by a unique type of cardiac involvement. Few studies have characterized echocardiographic (Echo) transitions from the indeterminate Chagas disease (ChD) form to CCM. The objective of this study was to identify the best cutoffs in multiple Echo parameters, speckle tracking, and N-terminal pro B-type natriuretic peptide (NT-proBNP) to distinguish patients without CCM (stage A) vs patients with myocardial involvement (stages B, C, or D). METHODS: Cross-sectional study conducted in 273 consecutive patients with different CCM stages. Echo parameters, NT-proBNP, and other clinical variables were measured. Logistic regression models (dichotomized in stage A versus B, C, and D) adjusted for age, sex, body mass index, and NT-proBNP were performed. RESULTS: Left ventricular global longitudinal strain (LV-GLS), mitral flow E velocity, LV mass index, and NT-proBNP identified early changes that differentiated stages A vs B, C, and D. The LV-GLS with a cutoff -20.5% showed the highest performance (AUC 92.99%; accuracy 84.56% and negative predictive value (NPV) 88.82%), which improved when it was additionally adjusted by NT-proBNP with a cutoff -20.0% (AUC 94.30%; accuracy 88.42% and NPV 93.55%). CONCLUSIONS: Our findings suggest that Echo parameters and NT-proBNP may be used as diagnostic variables in detecting the onset of myocardial alterations in patients with the indeterminate stage of ChD. LV-GLS was the more accurate measurement regarding stage A differentiation from the stages B, C, and D. Prospective longitudinal studies are needed to validate these findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".