{"id":"W2579344498","doi":"10.6084/m9.figshare.4530749.v1","title":"Emerging technologies in diagnostics for rare diseases, and the evolving world of patient discovery platforms","year":2017,"lang":"en","type":"article","venue":"Figshare","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Emerging technologies; Data science; Medicine; Artificial intelligence","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":[0.03582587,0.0007380465,0.0008138958,0.002951197,0.002699228,0.01234257,0.001988232,0.005453375,0.03896746],"category_scores_gemma":[0.03031766,0.0007341575,0.0008757597,0.003534462,0.007515321,0.01322668,0.004685126,0.01176185,0.01038163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007254064,"about_ca_system_score_gemma":0.008942213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005734182,"about_ca_topic_score_gemma":0.0232232,"domain_scores_codex":[0.9893628,0.003181696,0.0004475492,0.0008064026,0.005497156,0.000704398],"domain_scores_gemma":[0.9586352,0.02231325,0.001240566,0.002337584,0.009361858,0.006111482],"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":[0.0001356924,0.00001722475,0.0004657779,0.0006370225,0.00002483821,0.000512259,0.0004438262,0.0002291283,0.002518628,0.06631473,0.7492276,0.1794734],"study_design_scores_gemma":[0.00001411048,0.00001645692,0.0003334916,0.000299235,0.00000792721,0.0007164466,0.0003214716,0.0001543827,0.0006740581,0.02376814,0.9736685,0.00002574149],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.001849865,0.1680754,0.02666942,0.710687,0.0300255,0.0001279908,0.0008832087,0.0009483483,0.06073318],"genre_scores_gemma":[0.04523695,0.3440824,0.1019993,0.325472,0.06797642,0.0003310081,0.001968183,0.001742038,0.1111917],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03896746,"threshold_uncertainty_score":0.1894675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028972710292946,"score_gpt":0.2397904243191526,"score_spread":0.2295006972162232,"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."}}