{"id":"W2117784620","doi":"10.2196/medinform.3555","title":"Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 2: The Incorporation of SNP into the National Health Information System of Turkey","year":2014,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"National Health Interview Survey; SNP; Personalized medicine; Single-nucleotide polymorphism; Medicine; Computer science; Bioinformatics; Population; Environmental health; Biology; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004438063,0.0004343892,0.0004306739,0.0008281328,0.0004247058,0.002166845,0.001426517,0.0008506638,0.00198132],"category_scores_gemma":[0.006677368,0.0003242321,0.0007181639,0.001087861,0.0003188277,0.003232529,0.001460843,0.0006539919,0.0008920298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001169101,"about_ca_system_score_gemma":0.002752857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007833869,"about_ca_topic_score_gemma":0.004961063,"domain_scores_codex":[0.997144,0.0009060091,0.0004927684,0.0005590006,0.0006712,0.0002269955],"domain_scores_gemma":[0.9950365,0.001146757,0.0003072821,0.001099967,0.002142026,0.0002673966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002175991,0.0008842126,0.08050904,0.001639253,0.0003191223,0.003696772,0.002144948,0.01952544,0.1336883,0.01273505,0.01938828,0.7232936],"study_design_scores_gemma":[0.0004749612,0.004539383,0.1698746,0.001171575,0.001243054,0.00838939,0.004313909,0.2782691,0.2558966,0.009034525,0.2663548,0.0004382168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5934314,0.002246208,0.3584905,0.003552639,0.0004054835,0.0028019,0.005483655,0.01355365,0.02003467],"genre_scores_gemma":[0.5891008,0.001591804,0.3934318,0.0005758756,0.0000737193,0.0004504508,0.009204647,0.0002603968,0.005310512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007833869,"threshold_uncertainty_score":0.023471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03611285502303619,"score_gpt":0.3228478009892612,"score_spread":0.286734945966225,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}