Reference Values for Pulse Wave Doppler and Tissue Doppler Imaging in Pediatric Echocardiography
Bibliographic record
Abstract
BACKGROUND: In pediatric echocardiography, pulse wave Doppler, and tissue Doppler imaging velocities are widely used to assess cardiac function. Current reference values and Z scores, allowing adjustment for growth are limited by inconsistent methodologies and small sample size. Using a standardized approach for parametric modeling and Z score quality assessment, we propose new pediatric reference values and Z score equations for most left ventricular pulse wave Doppler and tissue Doppler imaging measurements. METHODS AND RESULTS: Two hundred thirty-three healthy pediatric subjects 1 to 18 years of age were prospectively recruited. Thirteen pulse wave Doppler and 14 tissue Doppler imaging measurements were recorded. Normalization for growth was done via a complete and standardized approach for parametric nonlinear regression modeling. Several analyses were performed to ensure adequate Z score distribution and to detect potential residual associations with growth or residual heteroscedasticity. Most measurements adopted a nonlinear relationship with growth and displayed significant heteroscedasticity. Compared with age, height, and weight, normalization for body surface area was most efficient in removing the effect of growth. Generally, polynomial and allometric models yielded adequate goodness-of-fit. Residual values for several measurements had significant departure from the normal distribution, which could be corrected using logarithmic or reciprocal transformation. Overall, weighted parametric nonlinear models allowed us to compute Z score equations with adequate normal distribution and without residual association with growth. CONCLUSIONS: We present Z scores for normalized pulse wave Doppler and tissue Doppler imaging in pediatric echocardiography. Further studies are needed to define the threshold beyond which health becomes a disease by integrating other important factors such as ventricular morphology, loading conditions, and heart rate.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".