Bibliographic record
Abstract
Quantitative echocardiographic assessment of systolic function in children with congenital heart disease is challenging due to the variability in cardiac morphology, loading conditions, and heart rate. Traditionally parameters reflecting changes in cavity dimensions and volumes (such as fractional shortening and ejection fraction) have been used for assessing left ventricular (LV) function. The introduction of more automated techniques for calculating ejection fraction based on two and three-dimensional images have improved the reproducibility and clinical utility of these measurements. Also the development of tissue Doppler and speckle-tracking echocardiography allow a more direct quantitative evaluation of myocardial motion and deformation. Extensive research has demonstrated these techniques can add important information on LV systolic function but their role in clinical practice is still being investigated. The quantitative assessment of right ventricular (RV) function is based on the utilization of different echocardiographic parameters. Systolic changes in RV dimensions can be studied by measuring fractional area change or by measuring RV volumes using three-dimensional echocardiography. For the RV longitudinal motion is more important and can be quantified by measuring tricuspid annular motion and longitudinal strain measurements. The same principles can be used for assessing systolic function of the single right and left ventricle but the interpretation of the quantitative parameters becomes more challenging for these more complex pumping chambers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".