{"id":"W4250636026","doi":"10.1158/1538-7445.am2019-2464","title":"Abstract 2464: Gabriella Miller Kids First Data Resource Center: Harmonizing clinical and genomic data to support childhood cancer and structural birth defect research","year":2019,"lang":"en","type":"article","venue":"Cancer Research","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Context (archaeology); Data sharing; Resource (disambiguation); Translational research; Medicine; Bioinformatics; Data science; Biology; Computer science; Pathology; Alternative medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.02280319,0.0009730713,0.001615046,0.005841592,0.002135417,0.006104589,0.006227699,0.002127662,0.07464647],"category_scores_gemma":[0.0680719,0.001097685,0.001242924,0.006459816,0.0009578336,0.004455997,0.01165527,0.004041439,0.04099809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003941827,"about_ca_system_score_gemma":0.01770396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01974616,"about_ca_topic_score_gemma":0.02323281,"domain_scores_codex":[0.9864618,0.004930472,0.00164348,0.002697245,0.003276817,0.0009901908],"domain_scores_gemma":[0.9013417,0.0207296,0.005513957,0.02703612,0.02003664,0.02534202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005919378,0.00009515139,0.007261924,0.0002911855,0.00007909637,0.0001809543,0.0002017016,0.0004798983,0.0006871616,0.004626879,0.951055,0.03444906],"study_design_scores_gemma":[0.001036891,0.0001845993,0.01616809,0.00103701,0.00008198255,0.0002938656,0.0004156526,0.003348214,0.001911034,0.007744377,0.9676458,0.0001323396],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0105051,0.002675446,0.02830989,0.06075665,0.00304035,0.003100802,0.7779147,0.03139832,0.08229877],"genre_scores_gemma":[0.04443947,0.002376199,0.1250152,0.01642693,0.002422198,0.005756887,0.7735422,0.009410237,0.02061076],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.07464647,"threshold_uncertainty_score":0.2497174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2243158981375733,"score_gpt":0.4517246155538782,"score_spread":0.2274087174163048,"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."}}