{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003351756,0.0002077899,0.0004584596,0.00004884423,0.0001027416,0.000006815825,0.0002081607,0.0002773885,0.00005239819],"category_scores_gemma":[0.0003162055,0.0001421813,0.0001534942,0.00006814328,0.0001545858,0.000004383673,0.0002099306,0.000165844,0.000009980001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001050372,"about_ca_system_score_gemma":0.000113421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001325543,"about_ca_topic_score_gemma":0.00001566047,"domain_scores_codex":[0.9981908,0.0004604598,0.0004431369,0.0004502126,0.0001322756,0.0003230674],"domain_scores_gemma":[0.9991592,0.0001426999,0.0002533304,0.0003028612,0.00004606259,0.00009579932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000376225,0.00006659114,0.001401292,0.00007466356,0.00003492518,0.00001429703,0.0001877499,0.007238844,0.9808003,0.00002961532,0.00004657615,0.00972896],"study_design_scores_gemma":[0.003875029,0.001335884,0.0001148997,0.0001979561,0.0001253016,0.0008321606,0.000270771,0.3225569,0.6658444,0.0001276738,0.004117643,0.000601328],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615191,0.001330879,0.035993,0.0001849842,0.0001495689,0.0002118715,0.00001113158,0.00003158144,0.0005679026],"genre_scores_gemma":[0.996988,0.0001249588,0.002352255,0.0001531926,0.0002330711,0.00001942292,0.00002358621,0.00003418428,0.00007131739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.315318,"threshold_uncertainty_score":0.5797986,"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."}}