{"id":"W4416291588","doi":"10.1038/s41698-025-01136-9","title":"Multiclass machine learning models for molecular subtype identification of pediatric low-grade glioma using bi-institutional MRIs for precision medicine","year":2025,"lang":"en","type":"article","venue":"npj Precision Oncology","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Vector Institute; Hospital for Sick Children; Mental Health Research Canada; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Random forest; Receiver operating characteristic; Magnetic resonance imaging; Confidence interval; Glioma; Identification (biology); Outlier; Fusion","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009068532,0.0002294914,0.0006724176,0.0008090917,0.0002141586,0.00001168966,0.0001563253,0.0003022454,0.00003480189],"category_scores_gemma":[0.001735473,0.0001858579,0.000232375,0.0006291114,0.0001110742,0.0001104846,0.0000793138,0.0001558122,0.000003502325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004045603,"about_ca_system_score_gemma":0.0003641832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006105893,"about_ca_topic_score_gemma":0.00001345453,"domain_scores_codex":[0.9977283,0.0001262801,0.0009460973,0.0005376426,0.0003727919,0.000288894],"domain_scores_gemma":[0.9969395,0.001482743,0.0004382011,0.000339943,0.000684683,0.0001149187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003625361,0.003316929,0.00999912,0.0006899884,0.0004688012,0.00006360858,0.0003582297,0.06974376,0.8192317,0.009467181,0.002155209,0.08088013],"study_design_scores_gemma":[0.01821729,0.002370526,0.004354064,0.0003699287,0.001639247,0.00004728599,0.0000786766,0.7909055,0.1657687,0.010301,0.005707913,0.0002399093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7138793,0.002411405,0.279617,0.001251326,0.0006339054,0.001959248,0.00005243838,0.00004101868,0.0001544207],"genre_scores_gemma":[0.9823629,0.0002794704,0.01640698,0.00009926394,0.0001746279,0.000295822,0.0002436681,0.00002727683,0.0001100095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7211617,"threshold_uncertainty_score":0.7579064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05449566757474415,"score_gpt":0.3723977593320438,"score_spread":0.3179020917572997,"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."}}