{"id":"W2957448468","doi":"10.1002/ijc.32561","title":"An eHealth decision‐support tool to prioritize referral practices for genetic evaluation of patients with Wilms tumor","year":2019,"lang":"en","type":"article","venue":"International Journal of Cancer","topic":"Renal and related cancers","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University Health Centre; McGill University; University of Toronto; SickKids Foundation; Hospital for Sick Children; Montreal Children's Hospital","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Fondation du cancer des Cèdres; Children's Hospital Foundation; Fondation de l'Hôpital de Montréal pour enfants; Pediatric Oncology Group of Ontario","keywords":"Referral; Medicine; eHealth; Wilms' tumor; Population; Genetic testing; Pediatrics; Family history; Health care; Internal medicine; Family medicine; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007251681,0.0006216665,0.0007816498,0.004006501,0.0005340363,0.002077312,0.001236362,0.0008125702,0.008721994],"category_scores_gemma":[0.04171843,0.0002946081,0.0007552092,0.002155295,0.0001670163,0.0009761871,0.001360225,0.0009498223,0.001385767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002040117,"about_ca_system_score_gemma":0.003126718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01038233,"about_ca_topic_score_gemma":0.01700586,"domain_scores_codex":[0.9954353,0.002307612,0.0009542826,0.0004081556,0.0006672152,0.0002273523],"domain_scores_gemma":[0.9669757,0.0239549,0.003153244,0.0006638448,0.003269661,0.001982595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002966054,0.001113005,0.3003746,0.0009176883,0.0003863489,0.0008699468,0.000800061,0.007326371,0.0005453164,0.001789965,0.1358067,0.547104],"study_design_scores_gemma":[0.007243669,0.003141793,0.492373,0.00475616,0.001571898,0.002610981,0.004161122,0.3211603,0.008255695,0.01329979,0.1407451,0.0006804281],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7258883,0.005891505,0.09148543,0.0319172,0.001117782,0.01132927,0.06521118,0.01419716,0.0529622],"genre_scores_gemma":[0.7475806,0.001439334,0.2216996,0.0032838,0.0004097084,0.003776714,0.01694828,0.0002185047,0.004643528],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01038233,"threshold_uncertainty_score":0.038351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01606529555177903,"score_gpt":0.3745811278091443,"score_spread":0.3585158322573652,"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."}}