VARF: Synthetic Audiogram Generator, Experiment Code and Systematic Review Materials
Abstract
Supporting materials for the manuscript "VARF: A Five-Layer AI Architecture for Virtual Aural Rehabilitation: Design and Algorithmic Feasibility", submitted to Engineering, Technology & Applied Science Research. Contents. A synthetic audiogram generator (deterministic, seed 20260301) and the complete code for four experiments: a hybrid rule-plus-gradient-boosted triage classifier, adversarial robustness checks, differentially private federated averaging with Renyi differential-privacy accounting, and a LinUCB adaptive difficulty controller benchmarked against a clinical staircase procedure. Also included: the generated dataset of 5,000 cases, a reward-weight sensitivity analysis, a privacy-accountant configuration scan, the systematic review materials (database-specific search strings for PubMed, Scopus, CINAHL and IEEE Xplore with all deviations recorded, and the PRISMA 2020 flow), and the six figures as published. Provenance. code/varf_reproduce.py is the authoritative specification of the method: every numerical value in Section III of the manuscript is its output. PROVENANCE.md records which reported values changed relative to the originally submitted version, and why. Important caveats. All results derive from synthetic data. No record-level patient data and no record-level NHANES data were used at any stage; the generator's parameters were calibrated to published summary statistics only, namely age- and frequency-stratified mean thresholds and standard deviations from…
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