P4.02 Assessing ventricular–vascular interactions non-invasively in healthy adolescents☆
Bibliographic record
Abstract
Background: Characterization of normal ventricular–vascular interactions by non-invasive assessment in healthy adolescents may provide new mechanistic insights into altered physiological states in congenital and acquired heart disease in this age group. Methods: Ventricular and vascular measures were non-invasively determined in 113 healthy adolescents (57 females, aged 10–18 years) on the same occasion. Ventricular assessment included standard echocardiographic M-mode and 2D volumetric techniques, and pulse and tissue Doppler imaging. Vascular assessment included carotid and brachial artery ultrasound, applanation tonometry and echo-Doppler of the aorta. Arterial (Ea) and left ventricular (LV) end-systolic (Ees) elastance were estimated noninvasively and their ratio used to assess ventricular-arterial coupling. Sample characteristics were assessed against a standard normal distribution. Relationships were tested using Pearson’s correlations. Statistical significance was considered at p<0.01. Results: All measures were normally distributed. Carotid intima-media thickness (CIMT) had significant positive correlations (see Table) with LV mass-indexed to BSA, LV mean velocity of circumferential fibre shortening corrected (VCFc), mitral valve (MV) pulse/tissue Doppler velocity ratio (E/E′) and Ea/Ees ratio, but a negative correlation with Ees. Central pulse wave velocity (PWV) around the aortic arch by echo-Doppler assessment had significant positive correlations with LV ejection fraction (EF) and myocardial performance index (MPI). Aortic PWV from carotid to femoral artery by applanation tonometry and flow-mediated dilatation (FMD) showed no significant correlations. Conclusions: Ventricular-vascular interactions were found with proximal rather than distal aortic and arterial structure and function. These non-invasively determined ventricular-vascular interactions may be of benefit in monitoring progression and therapeutic response in adolescent disease populations. Variable CIMT FMD Aortic PWV Central PWV Mean±SD 0.431±0.046 mm 7.4±3.1% 5.0±0.9 m/s 4.5±1.1 m/s LV EF r=+0.06, p=0.54 r=+0.07, p=0.53 r=−0.01, p=0.99 r=+0.26, p<0.01 61±6% LV VCFc r=+0.28, p<0.005 r=−0.06, p=0.57 r=+0.01, p=0.91 r=+0.19, p=0.05 1.13±0.17 circ/s LV mass-indexed r=+0.25, p<0.01 r=−0.08, p=0.42 r=−0.08, p=0.43 r=+0.09, p=0.37 65±12 g/m2 LV MPI r=+0.17, p=0.08 r=+0.05, p=0.63 r=+0.03, p=0.73 r=+0.30, p<0.01 0.3±0.09 MV E/E′ r=+0.25, p<0.01 r=−0.08, p=0.44 r=+0.06, p=0.56 r=+0.15, p=0.12 5.2±0.9 Ea r=−0.17, p=0.08 r=+0.06, p=0.55 r=−0.13, p=0.18 r=+0.15, p=0.11 1.63±0.44 mmHg/ml Ees r=−0.27, p<0.01 r=+0.08, p=0.40 r=−0.11, p=0.26 r=+0.07, p=0.46 3.13±1.01 mmHg/ml Ea/Ees r=+0.28, p<0.01 r=−0.14, p=0.17 r=+0.03, p=0.74 r=+0.09, p=0.36 0.54±0.12
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".