{"id":"W2884345273","doi":"10.1007/s13760-018-0973-1","title":"Personalized image-based tumor growth prediction in a convection–diffusion–reaction model","year":2018,"lang":"en","type":"article","venue":"Acta Neurologica Belgica","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Glioma; Personalized medicine; Magnetic resonance imaging; Medicine; Effective diffusion coefficient; Particle swarm optimization; Computer science; Artificial intelligence; Nuclear medicine; Radiology; Machine learning; Bioinformatics; Cancer research; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00064536,0.0003753329,0.0005259307,0.0002624394,0.0002252174,0.00004137479,0.0003685738,0.0002553442,0.0008179153],"category_scores_gemma":[0.002488401,0.000296995,0.0001660685,0.0005127726,0.0005844293,0.0002048481,0.00009712789,0.0005557957,0.0001887051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008961765,"about_ca_system_score_gemma":0.00007740001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001835178,"about_ca_topic_score_gemma":0.00001891801,"domain_scores_codex":[0.9972824,0.0003658092,0.0006543865,0.0007482109,0.0003732886,0.0005759312],"domain_scores_gemma":[0.9978965,0.0009032422,0.0003060041,0.0005013308,0.0002302875,0.0001626323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.004217006,0.005711957,0.01534612,0.0005953323,0.0001217371,0.0003512376,0.002147833,0.000009295167,0.784982,0.1553905,0.0309038,0.000223167],"study_design_scores_gemma":[0.007441669,0.004004393,0.02485906,0.0001321412,0.0002530107,0.0004667965,0.0001778528,0.347528,0.01587982,0.5958089,0.002188138,0.001260216],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785568,0.00000640482,0.004283546,0.002857466,0.0001924101,0.0007250661,0.00003309043,0.000562549,0.01278274],"genre_scores_gemma":[0.9890674,0.000004779148,0.00814839,0.001931612,0.0001592355,0.0001897665,0.0000175822,0.00004573603,0.0004355523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7691022,"threshold_uncertainty_score":0.9999482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03831402441411062,"score_gpt":0.2835910569107142,"score_spread":0.2452770324966036,"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."}}