Reproducibility Package for Model A0: Evidence-Constrained Early Evaluation of Health AI in Endometriosis

Juliana Almeida Oliveira, Márcia Cristina França Ferreira, Eduardo Batista Cândido, Ana Luiza Lunardi Rocha, Flávia Ribeiro de Oliveira, Agnaldo Lopes da Silva Filho
2026 · doi:10.5281/zenodo.22801661 · W7213413299
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This reproducibility package presents Model A0, a deterministic threshold model for early evaluation of AI-assisted endometriosis pathway activation in Brazil's health system, using Appropriate Endometriosis Pathway Activation as its primary operational outcome.

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The authors present a reproducibility package for Model A0, a deterministic threshold model designed to evaluate health artificial intelligence under evidence constraints. This framework was applied to assess AI-assisted endometriosis pathway activation within Brazil's Unified Health System, focusing on operational outcomes rather than clinical efficacy or economic value. The package includes computational specifications, R code, and classification logs that distinguish between parameterized evidence and excluded downstream data. This paper is centrally about endometriosis — specifically the methodological evaluation of AI tools for activating appropriate diagnostic pathways in a public health setting.

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Abstract

Reproducibility package for Model A0, a deterministic threshold model developed for evidence-constrained early evaluation of Health AI and applied to AI-assisted endometriosis pathway activation in Brazil's Unified Health System (SUS). Model A0 separates algorithm performance, reach, clinical translation, system realization, baseline usual-care pathway activation, and proximal pathway outcomes. The model uses Appropriate Endometriosis Pathway Activation (AEPA) as a model-defined proximal operational outcome and deliberately excludes unsupported downstream clinical and economic consequences. The package contains the frozen scientific Face Sheet, computational implementation specification, R code, model outputs, computational verification materials, evidence reference materials, and an Evidence Search and Classification Log. Evidence is classified according to its legitimate modeling role as parameterized evidence, contextual anchor, threshold variable, or excluded downstream evidence. Model A0 is an early health technology assessment and methodological proof-of-concept. It does not evaluate a specific commercial AI product and does not establish clinical effectiveness, cost-effectiveness, a value-based price, reimbursement recommendations, or readiness for adoption. No individual-level participant data were generated or analyzed. Version 4.1 — 16 September 2026
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Reproducibility Package for Model A0: Evidence-Constrained Early Evaluation of Health AI in Endometriosis Authors/Creators - 1. Federal University of Minas Gerais (UFMG) - 2. Maternidade Odete Valadares, Fundação Hospitalar do Estado de Minas Gerais (FHEMIG) Description Reproducibility package for Model A0, a deterministic threshold model developed for evidence-constrained early evaluation of Health AI and applied to AI-assisted endometriosis pathway activation in Brazil's Unified Health System (SUS). Model A0 separates algorithm performance, reach, clinical translation, system realization, baseline usual-care pathway activation, and proximal pathway outcomes. The model uses Appropriate Endometriosis Pathway Activation (AEPA) as a model-defined proximal operational outcome and deliberately excludes unsupported downstream clinical and economic consequences. The package contains the frozen scientific Face Sheet, computational implementation specification, R code, model outputs, computational verification materials, evidence reference materials, and an Evidence Search and Classification Log. Evidence is classified according to its legitimate modeling role as parameterized evidence, contextual anchor, threshold variable, or excluded downstream evidence. Model A0 is an early health technology assessment and methodological proof-of-concept. It does not evaluate a specific commercial AI product and does not establish clinical effectiveness, cost-effectiveness, a value-based price, reimbursement recommendations, or readiness for adoption. No individual-level participant data were generated or analyzed. Version 4.1 — 16 September 2026 Files ModelA0_Reproducibility_Package_v4.1_FINAL.zip Files (688.3 kB) | Name | Size | Download all | |---|---|---| | md5:d1c4e1ad642283cb4628838699b2eab3 | 688.3 kB | Preview Download |

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