A Novel Method of Expressing Left Ventricular Mass Relative to Body Size in Children
Bibliographic record
Abstract
BACKGROUND: Left ventricular (LV) hypertrophy (LVH) in children is widely defined as a left ventricular mass index (LVMI, g/m(2.7)) >95th percentile. However, LVMI increases with decreasing height in young children; thus, the 95th percentile LVMI will depend on the height distribution of the reference population. The objective of this study was to compare the performance of a novel method of expressing LV mass relative to body size (centile curves) with the LVMI method. METHODS AND RESULTS: LV mass was estimated by M-mode echocardiography in 440 healthy nonobese reference children (birth to 21 years) and 239 children at risk for LVH; the LVMI was calculated for all children. Three samples of 270 children, each with different height distributions, were drawn from the reference population. A sample-specific 95th percentile LVMI was determined for each reference sample. At-risk children were classified as having LVH or not based on each sample-specific 95th percentile. Four LV mass-for-height centile curves were constructed with the Cole lambda-mu-sigma method and data from each reference sample. At-risk children were each assigned an LV mass-for-height percentile with these curves and were reclassified as having LVH if LV mass-for-height was >95th percentile. The centile method provided a stable estimate of the proportion of at-risk children with LVH regardless of reference group, whereas proportion estimates varied significantly depending on the reference population when the LVMI method was used. CONCLUSIONS: LV mass-for-height centile curves are superior to LVMI as a method of normalizing LV mass to body size in children.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".