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Record W2530084627 · doi:10.1002/uog.16879

EP07.11: A novel customizable sensitivity and specificity driven standardization of early fetal echocardiography by <scp>TVS</scp> (transvaginal sonography)

2016· article· en· W2530084627 on OpenAlexaff
Shraga Rottem, Lisa K. Hornberger, James C. Huhta, Luís F. Gonçalves, E. Torgyekes, Jimmy Espinoza, M. Chen, Christian Macedonia

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2016
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineFetal echocardiographyDimensionality reductionSensitivity (control systems)Fetal heartFetusArtificial intelligenceComputer sciencePrenatal diagnosisPregnancy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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.

Opus teacher head0.009
GPT teacher head0.228
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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