{"id":"W2027576483","doi":"10.1159/000120511","title":"Brain Stem Gliomas: A Classification System Based on Magnetic Resonance Imaging","year":2008,"lang":"en","type":"article","venue":"Pediatric Neurosurgery","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":249,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Medicine; Hydrocephalus; Magnetic resonance imaging; Radiology; Brain tumor; Classification scheme; Pathology; Machine learning","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":[0.001460183,0.0004871417,0.0005748155,0.003148341,0.0003637145,0.0005094174,0.0003282286,0.000276001,0.001123742],"category_scores_gemma":[0.003386015,0.0002729076,0.0002894403,0.001435054,0.0005272555,0.0006680991,0.0006315199,0.0003335932,0.0005321071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004734335,"about_ca_system_score_gemma":0.0007022038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002088946,"about_ca_topic_score_gemma":0.002323794,"domain_scores_codex":[0.9992499,0.0001402715,0.0001903977,0.00008382121,0.0002274853,0.00010818],"domain_scores_gemma":[0.9977632,0.0004640316,0.0008363071,0.0001702092,0.000617992,0.0001483368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001248346,0.0000319307,0.9895545,0.00001956462,0.00001914336,0.0005318925,0.0001453645,0.000191665,0.000887307,0.00005964826,0.0002624268,0.008171749],"study_design_scores_gemma":[0.0000416195,0.0004060387,0.9860168,0.00002472401,0.00005641067,0.008073858,0.0007419333,0.002016126,0.001151568,0.0001231142,0.001328724,0.00001912837],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966207,0.0002261779,0.001465719,0.00002184429,0.00000797674,0.000159636,0.0006364469,0.0000213806,0.0008401559],"genre_scores_gemma":[0.9934472,0.0002283158,0.003768115,0.00001764496,0.00001405214,0.0002145477,0.00213391,0.0000145025,0.0001616996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003148341,"threshold_uncertainty_score":0.007722259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02583293299963544,"score_gpt":0.2366873314020891,"score_spread":0.2108543984024537,"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."}}