{"id":"W7084970857","doi":"10.6084/m9.figshare.30291991.v1","title":"Additional file 4 of Machine learning-based radiomics using magnetic resonance images for prediction of clinical complete response to neoadjuvant chemotherapy in patients with muscle-invasive bladder cancer","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Radiomics; Magnetic resonance imaging; Bladder cancer; Chemotherapy; Complete response; Patient data","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009731097,0.002248356,0.002056404,0.002085952,0.0006048529,0.00199168,0.002593719,0.002561447,0.4218491],"category_scores_gemma":[0.01120279,0.0006810526,0.002459731,0.002361611,0.0004152097,0.001279159,0.001305551,0.001428034,0.1107343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00120973,"about_ca_system_score_gemma":0.00183631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01286797,"about_ca_topic_score_gemma":0.02273596,"domain_scores_codex":[0.9993458,0.00008305514,0.000106826,0.0002548635,0.0001015733,0.00010771],"domain_scores_gemma":[0.9953995,0.002752969,0.0004114693,0.0005344449,0.0006784403,0.0002231447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008665513,0.0001755361,0.006319292,0.003705558,0.0002620626,0.000106906,0.00003992071,0.001071072,0.0002298306,0.0003638557,0.9787401,0.008119238],"study_design_scores_gemma":[0.01276882,0.0005989397,0.0698034,0.004069012,0.001161274,0.0009691772,0.0003453752,0.006067863,0.001956442,0.01127297,0.8906526,0.0003341337],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001844154,0.00002978681,0.00005165853,0.00003941741,0.00001614968,0.00001828723,0.9994053,0.0000924683,0.0001624253],"genre_scores_gemma":[0.002339668,0.0000736801,0.0006166436,0.0001310861,0.00003894613,0.0004286625,0.9946612,0.0001044057,0.00160564],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4218491,"threshold_uncertainty_score":0.8246621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02205543581009867,"score_gpt":0.268039697580402,"score_spread":0.2459842617703033,"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."}}