EP07.11: A novel customizable sensitivity and specificity driven standardization of early fetal echocardiography by <scp>TVS</scp> (transvaginal sonography)
Bibliographic record
Abstract
To develop a system standardizing the early scanning of the fetal heart with a user customizable high sensitivity and specificity when screening for the 18 most common types of CHDs. We used a multicentre time-oriented database augmented with cases from the literature containing 42 markers of 18 types of CHDs. We programmed the sensitivity and specificity of the screen at 95% using a two steps mathematics driven standardization method: 11w-14w6d and 18 to 20 weeks. TVS by 14w6d was a requirement to obtain sufficiently high resolution of the cardiac morphologic and biometric markers. A customizer allows the use of transabdominal sonography (TAS) until TVS based skilled are developed. The most important markers to reach 95% sensitivity and specificity were the most frequent Class I markers (early onset at constant GA) and the most frequent Class III markers (variable onset). The use of dimensionality reduction via intelligent agent technique rarely required visualization of more than a few markers at a time to correctly screen for several CHDs at once. The recognition of 42 markers targeting of a 95% sensitivity and specificity is key when screening for 18 types of CHDs as a two-step process including a second scan performed at 18 to 20 weeks. Dimensionality reduction allows for screening utilizing the fewest most efficient combinations of sonographic markers at any given time and consequently has the potential to significantly reduce sonography workload while targeting the highest quality screening. This novel method of standardizing fetal echo assisted by marker set dimensionality reduction with a customizable per CHD sensitivity and specificity is superior to the existing conventional views-based technique. The new method will reduce the required training time of sonographers to achieve mastery of CHD screening in contrast to the conventional views-based technique.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".