{"id":"W2432034468","doi":"10.1093/neuonc/now065.18","title":"AT-19SYSTEMATIC RADIOLOGICAL PHENOTYPING OF ATYPICAL TERATOID RHABDOID TUMOURS USING LANGUAGE MODELLED MACHINE LEARNING APPROACHES","year":2016,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Chromatin Remodeling and Cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"","keywords":"Radiological weapon; Computer science; Artificial intelligence; Natural language processing; Medicine; Radiology","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":[0.002865307,0.0006353123,0.0005065666,0.001833083,0.0003704173,0.002008124,0.001026255,0.000739819,0.00196763],"category_scores_gemma":[0.00918919,0.0002722605,0.001854309,0.001112903,0.0004818072,0.0008645869,0.001192945,0.0008787919,0.0008893302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002266513,"about_ca_system_score_gemma":0.002386615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007371891,"about_ca_topic_score_gemma":0.01317613,"domain_scores_codex":[0.9981257,0.0007928847,0.0002028497,0.0004698435,0.000316051,0.00009268687],"domain_scores_gemma":[0.9946452,0.002918302,0.000770737,0.000737971,0.0007410374,0.0001867138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009055138,0.0005386989,0.2362536,0.0008100594,0.0006008047,0.001379076,0.0007585358,0.4707218,0.01319222,0.01130628,0.01096744,0.252566],"study_design_scores_gemma":[0.0000491797,0.0002778957,0.03630384,0.0001271717,0.0001075749,0.0007935136,0.0002133661,0.9246479,0.006739863,0.02156848,0.00910184,0.00006934346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4957997,0.0009327561,0.4485527,0.001944522,0.00007717816,0.0008668283,0.04156236,0.004750743,0.005513147],"genre_scores_gemma":[0.8077086,0.0002638945,0.1561062,0.0002013062,0.00003129304,0.0006569028,0.03292337,0.0001488088,0.001959647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007371891,"threshold_uncertainty_score":0.01644474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03967846761888544,"score_gpt":0.2786656196088555,"score_spread":0.2389871519899701,"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."}}