IVF Daddies
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Decision Clarity System
Reference · Terminology · v2026.2
Definitions Ledger
Embryo Grading
A morphological assessment of embryo structure and symmetry during IVF. It evaluates visual quality but does not confirm chromosomal normality.
View Related Authority →PGT-A (Preimplantation Genetic Testing for Aneuploidy)
A genetic screening method used to assess chromosomal normality in embryos prior to transfer. It is distinct from morphological grading.
View Related Authority →Birthright Citizenship
The constitutional right under the 14th Amendment granting citizenship to individuals born on U.S. soil, subject to jurisdictional interpretation.
View Related Authority →Escrow (Surrogacy)
A third-party financial holding structure used to safeguard funds during a surrogacy arrangement to ensure contractual compliance.
View Related Authority →AI and Algorithm Vocabulary
AUC (Area Under Curve)
A statistical measure of a model's ability to distinguish between classes. In embryo selection AI, AUC measures how well a model differentiates embryos that resulted in implantation from those that did not, within a test dataset. Range: 0.5 (random) to 1.0 (perfect). A published AUC without confidence interval is incomplete disclosure.
View Related Reference →Morphokinetic Scoring
Automated analysis of embryo cell division timing using time-lapse imaging. Records when each division event occurs and compares timing to population-level distributions. This is not a prediction of implantation. It is a measurement of developmental timing.
View Related Reference →Polygenic Risk Score (PRS) in Embryo Selection
An AI-generated score estimating the probability that an embryo carries genetic variants associated with complex traits or conditions. As of 2026, clinical use of PRS for complex trait selection is contested and considered premature by major professional bodies. Distinct from PGT-A, which screens for chromosomal aneuploidy.
View Related Reference →Model Drift
Degradation of AI model performance when deployment conditions differ from training conditions. In IVF labs, triggers include culture media changes, incubator updates, imaging system upgrades, and protocol modifications.
View Related Reference →Automation Bias
The tendency to accept algorithmic recommendations without independent evaluation, particularly when AI output is presented as default or pre-selected. Automation bias is not eliminated by the existence of an override. Interface design determines whether override is practical.
View Related Reference →Training Data Diversity
The degree to which the dataset used to train an AI model represents demographic, geographic, and clinical diversity of the population it will be applied to. Models trained on narrow populations may perform worse for patients outside that population.
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