{"id":"W4409877584","doi":"10.1093/noajnl/vdaf079","title":"MultiCubeNet: Multitask deep learning for molecular subtyping and prognostic prediction in gliomas","year":2025,"lang":"en","type":"article","venue":"Neuro-Oncology Advances","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Subtyping; Deep learning; Glioma; Artificial intelligence; Computer science; Psychology; Natural language processing; Medicine; Cancer research; Programming language","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":[0.0001042099,0.0001378878,0.0002846278,0.0002395954,0.00009486747,0.00001010421,0.00003596851,0.0001036803,0.000005690877],"category_scores_gemma":[0.000907797,0.0001247211,0.00004761525,0.0002202079,0.00007521365,0.00008470835,0.00003201307,0.0001609152,0.000002582724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008244516,"about_ca_system_score_gemma":0.00004459615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009014757,"about_ca_topic_score_gemma":0.00008826447,"domain_scores_codex":[0.999032,0.0000710055,0.0002320728,0.0003546441,0.00007074757,0.0002395304],"domain_scores_gemma":[0.9989303,0.0007860766,0.0000670607,0.00009802257,0.000061299,0.00005722353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003811826,0.000475373,0.774746,0.0001958879,0.00006616386,0.0003614369,0.000156544,0.002384012,0.1077148,0.0003862643,0.0000267348,0.1131055],"study_design_scores_gemma":[0.02193036,0.004554315,0.7464079,0.0005755483,0.000739263,0.0002326424,0.0008136698,0.05052011,0.1057837,0.001207878,0.06688098,0.0003536077],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988465,0.007638475,0.0008580775,0.001100639,0.0002900762,0.001145326,0.000002924949,0.00006578884,0.0004336551],"genre_scores_gemma":[0.9958291,0.0004788542,0.002706452,0.0003802863,0.00003120239,0.0005243499,0.00001944769,0.00001389304,0.00001640963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1127519,"threshold_uncertainty_score":0.508598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00883450910957026,"score_gpt":0.2999173499171456,"score_spread":0.2910828408075753,"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."}}