{"id":"W4412163723","doi":"10.1158/1557-3265.aimachine-b003","title":"Abstract B003: Towards machine learning fairness in glioblastoma: An evaluation of protected attributes in publicly available clinical datasets","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Glioblastoma; Computer science; Medicine; Artificial intelligence; Cancer research","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06987613,0.0009737887,0.001058865,0.00240668,0.001643599,0.003808783,0.001979782,0.00216984,0.002740555],"category_scores_gemma":[0.1598689,0.0002992267,0.001543178,0.002475437,0.002124807,0.002658933,0.004197262,0.002052688,0.0009866229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00233146,"about_ca_system_score_gemma":0.003314042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00467961,"about_ca_topic_score_gemma":0.00348701,"domain_scores_codex":[0.9663468,0.02158146,0.002408951,0.004377484,0.004481244,0.0008040721],"domain_scores_gemma":[0.8875307,0.07725458,0.007196541,0.01595061,0.009504178,0.00256335],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01130202,0.002210843,0.4571647,0.002514795,0.003276301,0.000423282,0.00155381,0.1800362,0.004085534,0.0185894,0.07070757,0.2481354],"study_design_scores_gemma":[0.00203144,0.003743382,0.1792475,0.001318933,0.0007841909,0.00131994,0.001756934,0.7000131,0.01373634,0.05780996,0.03795727,0.0002809802],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8479655,0.005378732,0.09556662,0.007420559,0.0008334589,0.001396014,0.02852017,0.003149672,0.009769259],"genre_scores_gemma":[0.9109012,0.0004179963,0.05679914,0.00113379,0.0003005634,0.000603715,0.02861457,0.0001794245,0.001049451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9301239,"threshold_uncertainty_score":0.3695447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2714021157842422,"score_gpt":0.564677647575961,"score_spread":0.2932755317917188,"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."}}