Published December 9, 2025 | Version 4.1.0
Dataset Open

LAI-PrEP Bridge Period Decision Support Tool v4.1.0: Code, Configuration, and Supplementary Materials

Authors/Creators

  • 1. Nyx Dynamics, LLC

Description

Zenodo v4.1.0 Update - Complete Documentation

Update Date: December 12, 2025
Previous Version DOI: 10.5281/zenodo.17727117 (v2.1.0)
NEW Version DOI: 10.5281/zenodo.17873201 (v4.1.0)
Full URL: https://zenodo.org/record/17873201

What's New in v4.1.0

Manuscripts Uploaded (Viruses Journal Submission)

  1. lai_tool_final.tex - LAI-PrEP Computational Validation Manuscript

    • Complete computational validation at UNAIDS global scale (21.2M patients)
    • Progressive validation across 4 scales (1K, 1M, 10M, 21.2M)
    • Comprehensive edge case testing (18/18 pass rate, 100%)
    • Full Discussion section addressing AI suitability in healthcare

  2. Supplementary_File_S3_AI_Readiness_Healthcare.tex - Framework for Responsible AI Deployment

    • Critical examination of computational vs. clinical validity
    • Evidence quality assessment (Tier 1-3 classification)
    • Interpretability and algorithmic transparency analysis
    • Equity and health disparities considerations
    • Benefit-risk calculus and staged implementation framework

Supplementary Files

  1. Supplementary Materials (S1-S4)
    • S1: Machine-readable configuration files (JSON)
    • S2: Complete 21-intervention library with evidence synthesis
    • S3: AI Readiness framework (as detailed above)
    • S4: Code repository documentation and reproducibility instructions

Documentation Updates

  1. README with reproducibility instructions
  2. Updated LICENSE files
  3. Citation metadata (CITATION.cff)

Version History

Version Date DOI Focus Status
v2.1.0 Oct 24, 2025 10.5281/zenodo.17727117 Initial code release Superseded
v4.1.0 Dec 12, 2025 10.5281/zenodo.17873201 Viruses manuscript submission CURRENT

Key Metrics from v4.1.0 Manuscripts

Computational Validation Results

  • Sample scales: 1,000 → 1,000,000 → 10,000,000 → 21,200,000 patients
  • Algorithmic precision: ±0.018 percentage points (95% CI) at 21.2M scale
  • Precision improvement: 144-fold increase from 1K scale
  • Test pass rate: 18/18 edge cases (100%)
  • Convergence: Mean success rates stabilized by 1M patients

Primary Findings

  • Baseline bridge period success: 23.96% (95% CI: 23.94–23.98%)
  • With interventions: 43.50% (95% CI: 43.48–43.52%)
  • Relative improvement: 81.6%
  • Global impact: 4.1 million additional successful transitions

Population Disparities

  • PWID baseline: 10.36% (highest need)
  • MSM baseline: 33.11% (lowest need)
  • Disparity gap: 22.75 percentage points
  • PWID intervention benefit: +265% relative improvement
  • Adolescent benefit: +147% relative improvement

Regional Analysis (UNAIDS Global Scale)

  • Sub-Saharan Africa: 62% of global patients, 21.69% baseline success
  • Europe/Central Asia: 6% of patients, 29.33% baseline success
  • Regional equity gap: 7.64 percentage points
  • SSA relative improvement with interventions: +91.2%

Economic Projections

  • Annual HIV infections prevented: ~80,000–200,000 (midpoint: 100,000)
  • Lifetime treatment costs saved: $40 billion
  • Implementation cost: $19.1 billion
  • Annual ROI: 2.1:1
  • 5-year cumulative ROI: 10.5:1

Complete Manuscript Contents

Main Manuscript Structure

  • Title: Computational Validation of a Clinical Decision Support Algorithm for LAI-PrEP Bridge Period Navigation at UNAIDS Global Target Scale
  • Journal: Viruses (MDPI)
  • Article Type: Original research manuscript
  • Word Count: ~52,000 (including supplementary materials)
  • Tables: 16 total (main + supplementary)
  • Figures: 8 total (including supplementary)
  • References: 87 citations

Section Breakdown

Main Manuscript (lai_tool_final.tex):

  1. Introduction

    • LAI-PrEP promise and implementation challenges
    • Bridge period attrition crisis (47% failure rate)
    • Need for computational decision support
    • Study objectives and distinction between computational vs. clinical validity

  2. Materials and Methods

    • Evidence synthesis from >15,000 clinical trial participants
    • Algorithm development with three-tier evidence classification
    • Population-specific baseline rates
    • 21 structural barriers with quantified impacts
    • 21 evidence-based interventions with effect sizes
    • Configuration-driven software architecture
    • Mechanism diversity scoring algorithm
    • Synthetic population generation procedures
    • Intervention combination models (two-stage approach)
    • Progressive validation study design (4 tiers)
    • Comprehensive edge case testing (18 scenarios)
    • Outcome measures and statistical analysis
    • Software availability and data sharing

  3. Results

    • Progressive validation: Convergence and precision analysis
    • Unit test results across all validation tiers
    • Comprehensive edge case testing (100% pass rate)
    • Population-specific predictions vs. published trials
    • Population-specific intervention effects
    • Regional analysis at UNAIDS global scale
    • Barrier impact analysis with dose-response relationship
    • Risk stratification distribution
    • Global impact projections

  4. Discussion

    • Principal findings and contributions
    • Computational precision vs. clinical uncertainty
    • Framework for prospective clinical validation
    • Contextualization of findings
    • Strengths and limitations
    • AI Suitability for Healthcare: 5 Critical Questions
      • External validity and false confidence
      • Evidence quality and extrapolated parameters
      • Interpretability and clinical oversight
      • Equity and population heterogeneity
      • Benefit-risk calculus and staged implementation
    • Limitations of computational validation vs. real-world performance
    • Future directions

  5. Conclusions

    • Summary of computational validation achievements
    • Distinction between algorithmic and clinical readiness
    • Call for prospective validation and equity-focused implementation
    • Commitment to responsible AI deployment

Supplementary File S3: AI Readiness in Healthcare

Comprehensive 45-page framework addressing:

  1. External Validity

    • Computational precision ≠ clinical certainty
    • Mathematical vs. external vs. prospective validity
    • Synthetic data limitations
    • Staged implementation approach

  2. Evidence Quality

    • Tier 1 (direct LAI-PrEP): 8 interventions
    • Tier 2 (HIV prevention analogs): 9 interventions
    • Tier 3 (cross-field extrapolation): 4 interventions
    • Parameter uncertainty vs. computational precision
    • Dynamic evidence integration strategies

  3. Interpretability

    • Algorithmic transparency: How calculations work
    • Mechanistic reasoning: Why recommendations are made
    • Uncertainty quantification: Confidence intervals
    • Population-specific baselines
    • Interpretability paradox and error detection
    • Supporting clinical judgment over algorithmic certainty

  4. Equity and Heterogeneity

    • Aggregation bias in healthcare AI
    • Multi-dimensional stratification approach
    • Individual barrier assessment (13 barriers)
    • Algorithmic fairness considerations
    • Within-population heterogeneity recognition
    • Distributional impact assessment

  5. Benefit-Risk Calculus

    • Projected benefits: 4.1M transitions, 100K infections prevented
    • Implementation risks: Resource misallocation, false confidence, equity harm
    • Staged implementation framework:
      • Phase 1: Pilot validation (2-3 sites, 50-100 patients)
      • Phase 2: Multi-site validation (10-15 sites, 500-1000 patients)
      • Phase 3: Scaled implementation with continuous monitoring

  6. Limitations of Computational Validation

    • Simulation vs. reality differences
    • Parameter uncertainty vs. computational precision
    • Context-specificity of parameters
    • Path forward: Prospective validation and continuous refinement

🔗 Updated Citations for Manuscripts

For Data Availability Statements

APA Format:

Demidont, A. C. LAI-PrEP bridge period decision 
support tool: Computational validation at UNAIDS global scale [Computer 
software]. Zenodo. https://doi.org/10.5281/zenodo.17873201

BibTeX Format:

@software{demidont2025laiprep,
  author    = {Demidont, Adrian C},
  title     = {LAI-PrEP Bridge Period Decision Support Tool},
  subtitle  = {Computational Validation at UNAIDS Global Scale},
  version   = {4.1.0},
  year      = 2025,
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.17873201},
  url       = {https://doi.org/10.5281/zenodo.17873201}
}

Chicago Manual of Style (Notes-Bibliography):

Demidont, Adrian C. "LAI-PrEP Bridge Period Decision 
Support Tool: Computational Validation at UNAIDS Global Scale." Zenodo. 
December 12, 2025. https://doi.org/10.5281/zenodo.17873201.

Vancouver Format:

Demidont AC. LAI-PrEP bridge period decision support tool: 
Computational validation at UNAIDS global scale [computer software]. 
Zenodo. 2025. https://doi.org/10.5281/zenodo.17873201

Nature Format:

Demidont, A. C LAI-PrEP bridge period decision support tool: 
Computational validation at UNAIDS global scale. Zenodo https://doi.org/10.5281/zenodo.17873201 (2025).

MLA Format (9th Edition):

Demidont, Adrian C. "LAI-PrEP Bridge Period Decision 
Support Tool: Computational Validation at UNAIDS Global Scale." Version 4.1.0, 
Zenodo, 12 Dec. 2025, doi.org/10.5281/zenodo.17873201.

📄 Manuscript LaTeX References Updated

In Data Availability Statement:

\section*{Data Availability Statement}
All code, configuration files, validation datasets, and supplementary materials 
are publicly available on Zenodo (DOI: \url{https://zenodo.org/record/17873201}) 
and GitHub Repository \url{https://github.com/Nyx-Dynamics/lai-prep-bridge-tool-pub}
(release v4.1.0, commit: [current-commit-hash]).

In GitHub Badge Section (if applicable):

\includegraphics[alt={DOI}]{https://zenodo.org/badge/DOI/10.5281/zenodo.17873201.svg}

 

Files in v4.1.0 Zenodo Record

Manuscripts

  • lai_tool_final.tex (52KB)
  • Supplementary_File_S3_AI_Readiness_Healthcare.tex (45KB)
  • [Additional supplementary files S1, S2, S4 as applicable]

Software

  • lai_prep_decision_tool_v2_1.py (850 lines)
  • lai_prep_config.json (configuration with 21 interventions)
  • test_edge_cases.py (18 test scenarios)
  • Validation result JSONs (1K, 1M, 10M, 21.2M scales)

Documentation

  • README.md (installation, usage, reproducibility)
  • LICENSE.md (MIT + CC-BY 4.0)
  • CITATION.cff (machine-readable citations)
  • CONTRIBUTING.md (if applicable)

 

Contact & Citation Information

For Questions About:

  • Algorithm: See Methods section in manuscript
  • Validation: See Results section with tables and figures
  • Implementation: See Discussion section, particularly AI Suitability subsection
  • Code reproduction: See Supplementary File S4 and GitHub README

Citation Template for Other Researchers:

When using this tool, please cite:

Demidont AC. Computational validation of a clinical decision 
support algorithm for LAI-PrEP bridge period navigation at UNAIDS global 
target scale. Viruses. 2025;[volume]:[article]. 
https://doi.org/10.5281/zenodo.17873201

Summary of v4.1.0

What's New:

  • Complete Viruses journal submission manuscripts
  • Comprehensive AI readiness framework (S3 supplement)
  • Full code repository with 100% test pass rate
  • Complete documentation for reproducibility
  • Updated evidence synthesis with 87 citations

Why It Matters:

  • Establishes algorithmic readiness for prospective clinical validation
  • Provides transparent framework for responsible AI deployment in healthcare
  • Demonstrates unprecedented computational rigor in HIV prevention tool validation
  • Bridges implementation science and AI with explicit equity focus
  • Ready for peer review and potential global implementation

Next Phase: Prospective clinical validation with 2-3 diverse sites (50-100 patients) to compare algorithmic predictions with real-world outcomes and refine parameters based on implementation data.

Status: ✅ COMPLETE AND SUBMITTED TO ZENODO
DOI: 10.5281/zenodo.17873201
Version: 4.1.0
Date: December 12, 2025
Ready for Viruses Journal Submission

Technical info

** Zenodo on all historical updates of datasets requiring new Zenodo doi documented in Zenodo ChangeLog.

** all Zenodo dataset pushes and dataset updates documented for review in update for Transparency and Reproducibility.

 

 

** Licensing Excludes Pharamceutical/Manufacturer Use without express written permission from Author for use.

Files

btg_bridge_tool_zenodo_package.zip

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Additional details

Dates

Submitted
2025-10-23
Dataset Submitted to Zenodo
Copyrighted
2025-10-23
Manuscript "Computational Validation of a Clinical Decision Support Algorithm for Long-Acting Injectable PrEP Bridge Period Navigation at UNAIDS Global Target Scale" and Supplementary Files S1-S8; Core Algorithm (lai_prep_decision_tool_v2_1.py, 850 lines): Population-specific risk stratification, barrier impact quantification, intervention recommendation with mechanism diversity scoring External Configuration (lai_prep_config.json, 558 lines): 7 populations, 13 barriers, 21 evidence-based interventions with literature-derived effect sizes Comprehensive Test Suite (test_edge_cases.py, 18 scenarios): 100% pass rate covering clinical edge cases, mathematical validation, mechanism diversity, data export, and error handling Validation Scripts: Progressive validation from 1K to 21.2M patients Documentation: Installation guides, API reference, integration instructions, contributing guidelines Example Data: Individual patient JSON template, batch CSV with 10 diverse scenarios Command-Line Interface (cli.py): Single patient assessment, batch processing, configuration validation
Created
2025-10-23
Zenodo Dataset related to original research: Computational Validation of a Clinical Decision Support Algorithm for Long-Acting Injectable PrEP Bridge Period Navigation at UNAIDS Global Target Scale
Updated
2025-11-30
Final Dataset Package for Viruses Submission
Updated
2025-12-03
Additional Configuration and Manuscript Revisions
Updated
2025-12-09
Manuscript Updates ONLY

Software

Repository URL
https://github.com/Nyx-Dynamics/lai-prep-bridge-tool-pub
Programming language
Python
Development Status
Active

References

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