Practical Implications of Obesity for Cardiovascular Diagnostics
Bibliographic record
Abstract
Obesity, one of the most prevalent and pressing global health challenges, has significant implications for cardiovascular disease (CVD) morbidity and mortality. Diagnostic testing for CVD is substantially affected by obesity, which affects test selection and execution and complicates the interpretation of diagnostic testing. Structural and metabolic changes in individuals with obesity create both physiological alterations and technical limitations across diagnostic modalities, as obesity alters cardiovascular (CV) structure, function, and diagnostic test accuracy. This review explores the practical implications of obesity for CV diagnostics, including physical examination, electrocardiogram interpretation, echocardiography, nuclear stress imaging, computed tomography, positron emission tomography, coronary calcium scoring, and magnetic resonance imaging. Although each modality faces unique challenges in individuals with obesity, modern techniques, a systematic approach, and individualized protocols can greatly enhance test performance and diagnostic yield. This review aims to provide a comprehensive narrative within a practical framework for optimizing CV diagnostics in patients with obesity to optimize CV testing in this growing population. We deal in detail with each investigative modality, providing an overview of the specific considerations in patients with obesity, along with practical recommendations for test selection, application, and interpretation. With an organized and personalized approach, diagnostic testing for CVD can be greatly improved in this specific, rapidly growing, and clinically important population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".