{"id":"W6997282109","doi":"","title":"Using Geographic Information Systems to Understand Utilization and Access to Prenatal Genetic Services (PGS).","year":2017,"lang":"en","type":"article","venue":"Florence Research (University of Florence)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geographic information system; Genetic counseling; Population; Catchment area; Geocoding; Rural area; Epidemiology; Prenatal care; Service (business)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003688143,0.0005073734,0.0004209057,0.009888246,0.0007321712,0.002146042,0.001263178,0.0004477777,0.009167781],"category_scores_gemma":[0.01931516,0.0003993505,0.0007510376,0.02205021,0.0003913105,0.001543193,0.002130099,0.0007759394,0.001691839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007312218,"about_ca_system_score_gemma":0.01578261,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8829132,"about_ca_topic_score_gemma":0.8745368,"domain_scores_codex":[0.9976357,0.0005526266,0.0003386697,0.0002781053,0.0009201678,0.0002747222],"domain_scores_gemma":[0.9903899,0.002836707,0.001763214,0.0008069923,0.003545153,0.0006580217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008095508,0.0000508563,0.5285028,0.001707397,0.0003733485,0.0001594017,0.003536399,0.004641245,0.0003367163,0.01224891,0.2108473,0.2375147],"study_design_scores_gemma":[0.00003617828,0.00003907952,0.7473744,0.001618173,0.0001999646,0.0001188164,0.005728462,0.005355129,0.0004241531,0.004754924,0.2342485,0.0001022329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.08713576,0.009633562,0.02416179,0.01205024,0.0004230599,0.001073663,0.787735,0.001861438,0.07592559],"genre_scores_gemma":[0.4512946,0.01221347,0.08243932,0.001216741,0.0001931673,0.001449099,0.4367505,0.000308678,0.01413446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8829132,"threshold_uncertainty_score":0.2355526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1015653081535118,"score_gpt":0.3444467074207514,"score_spread":0.2428813992672396,"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."}}