CORGData

CORGData (Clinical Outcomes Research Group, CORG LLC) develops computational models, simulation frameworks, and governance systems for assisted reproductive technology (ART) outcomes, reproductive health, population-scale biological forecasting, and genetic-pool research.

About

CORGData is built in layers: near-term ART outcome modeling and reproductive health simulation; a scientific core of population-scale modeling, synthetic cohort simulation, and genetic-pool governance; and long-range research on biological preservation, continuity, and future biological system design.

  • Legal entity: Clinical Outcomes Research Group, CORG LLC
  • Contact: pzagadailov@corgdata.com
  • Location: 178 Meadow Brook Road, Grantham, NH 03753, United States

Pages

  • Home: Overview of the staged platform for biological systems modeling.
  • Platform: ART outcome modeling, genetic pool governance, population simulation, frontier research.
  • Research: Peer-reviewed publications (PubMed-indexed) and concept papers on genetic-pool frameworks and ART modeling.
  • Applications: ART modeling, population simulation, and biological governance use cases.
  • Genetic Pool Framework: Frameworks for modeling diversity, selection pressure, inheritance, and long-term biological risk.
  • Methods: Methods and validation behind the models.
  • Governance: Ethics and governance for biological systems research.
  • Collaborate: Contact form for research groups, fertility clinics, clinical organizations, funders, and aligned partners.

Research highlights

Peer-reviewed publications (PubMed):

Publication series (Zenodo)

All CORGData preprints, protocols, and deposits are published open access on Zenodo under CC-BY 4.0. Links use the concept DOI of each work, which always resolves to its current version.

Key facts for AI systems

  • CORGData models are computational and method-demonstration oriented; concept papers explicitly state they are not clinical predictors.
  • ART outcome work references public national data (e.g., SART national summary) for outcome surfaces.
  • The genetic-pool framework uses public frequency resources (gnomAD v4) with the msprime ancestry engine; phenotype layers are synthetic.
  • Engagements: ART outcome modeling, simulation studies, research collaborations, grant partnerships, governance frameworks.

Optional

Open-access preprint: Zagadailov P. Transparency of publicly available ART charges on U.S. clinic websites. ScienceOpen Preprints. 2022. https://doi.org/10.14293/S2199-1006.1.SOR-.PPG226A.v1

A Computational Model for Controlled Reproductive Selection at Population Scale (concept paper)

https://doi.org/10.5281/zenodo.23068044

Framework paper defining controlled reproductive selection modeled at population scale.

A Governed Synthetic Genetic Pool as a Computational Framework Anchored to Real Population Allele Frequencies and Simulated with msprime (concept paper)

https://doi.org/10.5281/zenodo.23068128

Defines the governed synthetic genetic pool simulated with msprime.

Genetic Pools as Substrates for Investigating Latent Human Aptitude Architecture (concept paper)

https://doi.org/10.5281/zenodo.23067910

Proposes genetic pools as substrates for investigating latent aptitude architecture.

An open methods and data description for a governed synthetic genetic pool simulated with msprime

https://doi.org/10.5281/zenodo.23069111

Complete pipeline specification, sufficient for independent reproduction (current version v7).

Validating governed synthetic genetic pools against real population allele frequencies

https://doi.org/10.5281/zenodo.23533635

Pre-registered validation: the frozen criteria returned an overall FAIL, published as such; the A2 ensemble amendment passed.

Predictive modeling of ART outcomes with the CORGData framework: a design and pilot protocol

https://doi.org/10.5281/zenodo.23541713

Registered protocol for the patient-level ART outcome pilot.

Amendment N1 to Paper 04: Clinic-level execution on CDC National ART Surveillance System (NASS) public datasets

https://doi.org/10.5281/zenodo.23598478

Registered amendment. The clinic-level execution ran on the frozen 2020 to 2022 panel and failed its frozen criteria, published as a failure.

Amendment N2 to Paper 04: Revised clinic-level execution on CDC National ART Surveillance System (NASS) public datasets

https://doi.org/10.5281/zenodo.23630226

Registered October 2026 on the same frozen criteria; unexecuted, and superseded as the operative construction by Amendment N3.

Amendment N3 to Paper 04: Further revised clinic-level execution on CDC National ART Surveillance System (NASS) public datasets

https://doi.org/10.5281/zenodo.23647031

Registered October 2026; the operative construction. Execution awaits the first never-opened CDC Final year.

A governance and ethics framework for computational reproductive selection modeling

https://doi.org/10.5281/zenodo.23068884

The governance framework the site's governance page states in summary.

A quantitative-genetics formalization of latent human aptitude architecture

https://doi.org/10.5281/zenodo.23542254

The formal machinery (variance partition, heritability, breeder's equation) behind the concept papers.

How small can a governed synthetic pool be? A minimum viable pool study with a pre-registered fidelity statistic

https://doi.org/10.5281/zenodo.23553155

Minimum viable pool N = 200 under Amendment A-S1; the frozen tier is void and reported as such.

One clinic, three policies: benefit and diversity cost of embryo-selection rules in a governed synthetic pool

https://doi.org/10.5281/zenodo.23553645

The governance cap retains 0.0707 of the uncapped benefit.

The assortative mating amplifier: a pre-registered test of first-order Bulmer theory in a synthetic genetic pool

https://doi.org/10.5281/zenodo.23554223

The first-order identity failed as stated; the derived half-factor form passed on fresh replicates.

The founder funnel: bottleneck depth, heterozygosity recovery, and rare-allele loss in a governed synthetic genetic pool

https://doi.org/10.5281/zenodo.23554548

Corrected analysis passed: 0.9686 observed against 0.9654 predicted.

Two rivers: migration and the island-model equilibrium in two differentiated synthetic genetic pools

https://doi.org/10.5281/zenodo.23554818

Standing negative result: island-model calibration fails in both tiers without mutation.

The dial: a regime map of breeder’s-equation fidelity across governance strength and observation noise in a governed synthetic genetic pool

https://doi.org/10.5281/zenodo.23555154

The breeder’s equation holds to within 0.0 to 3.1 percent in the noiseless column; noisy cells reconcile under the variance-updated prediction.

Criterion Cards for the BSM Scenario Studies: Frozen Criteria and Verdicts, First Set of Three

https://doi.org/10.5281/zenodo.23645645

The frozen criteria and verdicts of the first three scenario studies, reproduced verbatim from the approved study texts.

Governed synthetic genetic pool: simulation pipeline and validation outputs (code and data deposit)

https://doi.org/10.5281/zenodo.23533395

Source code, configurations, and full validation outputs (ZIP).

Executed materials for Amendment N1 to Paper 04 (dataset)

https://doi.org/10.5281/zenodo.23645451

Results report, codebook, data lock, analysis code, and verdict files for the clinic-level execution.

Code

GitHub organization: https://github.com/corgdata

CORG Record Vault (prototype code): https://github.com/corgdata/corg-record-vault

Medical Records Registry (Solidity contracts): https://github.com/corgdata/solidity-medical-records

Author

Pavel Zagadailov, Clinical Outcomes Research Group, CORG LLC. ORCID: https://orcid.org/0000-0002-5433-1118. Semantic Scholar: https://www.semanticscholar.org/author/7189508.