Extracardiac factors influencing left ventricular hypertrophy classification models
Bibliographic record
Abstract
Extracardiac factors are known to influence electrocardiographic (ECG) features used in left ventricular hypertrophy (LVH) classification criteria. It is generally assumed that ECG amplitudes decrease with age and obesity. The authors examined the association between anthropometric variables and the standard 12-lead resting ECG of white, black and Hispanic men and women and found that, contrary to the expectations, many ECG amplitudes such as RaVL+SV3 increased with increasing body mass index (BMI) and changed little with age. As expected, obesity was associated with leftward shift of QRS axis. However, the leftward shift of about 8 degrees per decade of age was equally pronounced in overweight and normal-weight women and men. The apparent strong influence of chest size and configuration on ECG amplitudes is largely due to variations in body weight and heart size. Smaller QRS amplitudes in left lateral chest leads in women appear partially to be associated with the attenuating effect of the breast tissue. It is concluded that chest size and configuration, and body weight have a strong influence on ECG amplitudes used in LVH criteria and that this influence differs from lead to lead and the interactions are complex. Extracardiac anthropometric factors, age, gender, and ethnic differences need to be properly considered in order to improve LVH criteria and ECG models for left ventricular mass estimation.>
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".