Persian medical natural language processing remains limited by the scarcity of domain-specific resources and insufficient public characterization of their coverage and quality. We present Gaokerena, an openly released suite of Persian medical resources comprising an 85.5-million-word corpus of 101,954 web articles; MF3QA, a 24,000-pair patient-doctor question-answering dataset; 1,878 Iranian medical examination questions (KOPP); 1,089 manually translated medical MMLU questions (FA_MED_MMLU); and approximately 18,000 machine-translated MedMCQA training items filtered by two language-model evaluators. We characterize the resources' composition, orthographic variation, redundancy, topical coverage, privacy indicators, answer-key balance, extraction artefacts, and translation limitations. As application case studies, we adapt Aya-Expanse-8B into Gaokerena-V, trained on the corpus and MF3QA, and Gaokerena-R, developed through preference-based reasoning distillation. On translated medical MMLU, Gaokerena-V achieves 49.31% accuracy under direct prompting, compared with 46.65% for Aya-Expanse-8B; the paired exact McNemar test is statistically inconclusive (p = 0.0502). Gaokerena-R reaches 52.80% using five-sample self-consistency and a verifier, but this higher-compute setting is not directly comparable. Under matched five-sample chain-of-thought self-consistency without a verifier, Gaokerena-R scores 48.58%, versus 46.92% for Aya-Expanse-8B, a non-significant difference (p = 0.190). English evaluation also reveals a substantial performance gap relative to Persian. To our knowledge, Gaokerena provides the first openly released and systematically characterized suite of Persian medical resources alongside generative models built on them. These protocol-specific results do not establish an isolated training advantage or clinical readiness.

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