{"id":"W4388589521","doi":"10.1093/neuonc/noad179.0127","title":"CNSC-44. BIOLOGICAL PATTERN DISCOVERY IN GLIOBLASTOMA","year":2023,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Structural Genomics Consortium; Ontario Brain Institute; SickKids Foundation; Centre for Global Health Research; University of Toronto; University Health Network","funders":"","keywords":"Glioblastoma; Phenotype; Pixel; Computational biology; Biology; Computer science; Gene; Pattern recognition (psychology); Neuroscience; Artificial intelligence; Genetics; Cancer research","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":[0.0008584723,0.001016205,0.00080983,0.002603653,0.0005099132,0.0008958209,0.001068578,0.0008176784,0.005025059],"category_scores_gemma":[0.001776319,0.0002513447,0.001079498,0.001548735,0.0002926995,0.0003152502,0.0008428837,0.0004238856,0.003673812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363428,"about_ca_system_score_gemma":0.002259556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0291304,"about_ca_topic_score_gemma":0.03584201,"domain_scores_codex":[0.9990357,0.0001209369,0.00005445583,0.0002346405,0.0004196549,0.0001346333],"domain_scores_gemma":[0.9994131,0.0000926673,0.00006568953,0.00008882624,0.0002491631,0.0000906627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002040742,0.000932988,0.04071189,0.002812225,0.0006894442,0.0009778758,0.0001737518,0.04287611,0.0924813,0.003926067,0.4274813,0.3848963],"study_design_scores_gemma":[0.0006695992,0.00106299,0.0950724,0.000269537,0.0003402177,0.001183667,0.0003118768,0.5869756,0.09413192,0.005765486,0.2140715,0.0001452856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5526719,0.01001231,0.04382967,0.002106727,0.001191422,0.001388747,0.3006725,0.05878878,0.02933795],"genre_scores_gemma":[0.3837619,0.001794929,0.1078921,0.0006335814,0.0001659062,0.0008740076,0.4900678,0.001567569,0.0132422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0291304,"threshold_uncertainty_score":0.05792171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515787318139231,"score_gpt":0.295798267752786,"score_spread":0.2806403945713937,"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."}}