{"id":"W3009334705","doi":"10.3390/cancers12030578","title":"Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"Cancers","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Digital pathology; Gray level; Glioma; Grading (engineering); Artificial intelligence; Medicine; Random forest; Digital image analysis; Pathology; Atypia; Support vector machine; Computer science; Computer vision; Cancer research; Biology","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":[0.0001254072,0.0001216638,0.0004918134,0.0002443395,0.00006133799,0.00001947588,0.00008562655,0.00004744074,0.000115509],"category_scores_gemma":[0.0004527802,0.0001108399,0.0001972936,0.0009641986,0.0001489133,0.00007894976,0.00004661102,0.0003593489,0.000002757881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001950165,"about_ca_system_score_gemma":0.0001145806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001940344,"about_ca_topic_score_gemma":0.000001517586,"domain_scores_codex":[0.9990474,0.00003466934,0.0002598733,0.000254979,0.0001882611,0.0002147921],"domain_scores_gemma":[0.999418,0.00006221885,0.0001455433,0.0001163955,0.0000513777,0.000206494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002047415,0.00002290287,0.501869,0.0002146999,0.002029057,0.001060296,0.001905911,0.07254492,0.3869301,0.00006197667,0.0001866061,0.03296979],"study_design_scores_gemma":[0.0007376221,0.0001630582,0.003316534,0.00004795825,0.001457738,0.0001008355,0.0001444829,0.9894845,0.003246872,0.00002239791,0.001136012,0.0001419685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9547102,0.0008867428,0.04246951,0.001107672,0.00008905832,0.00007459701,0.00001307222,0.00006352706,0.0005855698],"genre_scores_gemma":[0.9969916,0.00005054476,0.002218885,0.0005027062,0.000125919,0.000001529631,0.00005115272,0.00002371518,0.00003397599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9169396,"threshold_uncertainty_score":0.4519921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01852469626171952,"score_gpt":0.2985756986660744,"score_spread":0.2800510024043549,"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."}}