{"id":"W2025993205","doi":"10.1016/j.acra.2005.02.022","title":"Technical and Practical Considerations for Permeability Modeling of Dynamic Contrast Enhanced MRI","year":2005,"lang":"en","type":"article","venue":"Academic Radiology","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dynamic contrast-enhanced MRI; Dynamic contrast; Angiogenesis; Medicine; Computer science; Biomedical engineering; Nuclear medicine; Magnetic resonance imaging; Medical physics; Radiology; Internal medicine","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":[0.0004819485,0.0001089453,0.0004349545,0.0001184499,0.00004082278,0.000001904193,0.00003003386,0.0004179912,0.0000732167],"category_scores_gemma":[0.002011202,0.0000997621,0.0000601399,0.00008547861,0.0003367727,0.00007249417,0.00002519057,0.0005180349,0.000002334317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001129143,"about_ca_system_score_gemma":0.0001787372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008910679,"about_ca_topic_score_gemma":0.00002994845,"domain_scores_codex":[0.9988099,0.00006705324,0.0004781809,0.0003268099,0.00008170103,0.0002363134],"domain_scores_gemma":[0.9974275,0.002061039,0.00009559118,0.0002008581,0.0001121184,0.000102865],"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":[0.001444214,0.0004358868,0.008478145,0.0004345903,0.0002914985,0.0000151925,0.001574194,0.004633354,0.901955,0.03856037,0.03310993,0.009067621],"study_design_scores_gemma":[0.009263737,0.001551741,0.008442878,0.0002340864,0.0008907465,0.006183521,0.0003566529,0.9007626,0.03142744,0.03115949,0.009148162,0.0005789395],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7844746,0.001360831,0.105982,0.1061535,0.0001138995,0.00127228,0.00003709085,0.00007221446,0.0005336493],"genre_scores_gemma":[0.9377679,0.003038652,0.05745748,0.001343612,0.0001669198,0.0001868272,0.000009089094,0.00001310144,0.00001644588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8961293,"threshold_uncertainty_score":0.406818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04567470590895275,"score_gpt":0.3858031590151744,"score_spread":0.3401284531062217,"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."}}