Parâmetros Ecocardiográficos Simples são Fortes Preditores de Risco Cardiovascular em Indivíduos Assintomáticos: Coorte Elsa-Brasil
Bibliographic record
Abstract
BACKGROUND: Several studies have evaluated echocardiographic abnormalities as predictors of cardiovascular risk; however, none have associated the global cardiovascular risk with echocardiographic abnormalities in the Brazilian population. OBJECTIVE: This study evaluates the association between the global cardiovascular risk (ASCVD score) and three echocardiographic abnormalities: left ventricular hypertrophy (LVH), left ventricular diastolic dysfunction (LVDD), and increased left atrium (LA) volume. METHODS: The study population was composed of participants from ELSA-Brasil who underwent echocardiography between 2008 and 2010 (n = 2973). They were asymptomatic and had no history of cardiovascular disease. The ASCVD score was calculated in two periods: 2008-2010 and 2012-2014. Prevalence ratios (PR) were estimated with 95% confidence intervals (CI). RESULTS: There is an association between echocardiographic abnormalities and high global cardiovascular risk (ASCVD score ≥ 7.5) in both study periods, separately. The combined global risk (low risk in the first period and high risk in the second period) was significantly associated only with LVDD (PR = 3.68, CI 95% 2.63-5.15) and LVH (PR = 2.20, 95% CI 1.62-3.00). CONCLUSION: Echocardiographic abnormalities (LVDD, LVH, and increased LA volume) are independent predictors of cardiovascular risk in Brazilian adults.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".