{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001752121,0.001024133,0.0009574728,0.0006195782,0.001125804,0.00281479,0.002276853,0.001946952,0.0050674],"category_scores_gemma":[0.01044916,0.0008185351,0.0006319525,0.0005634949,0.0008608207,0.003455107,0.001171863,0.00189539,0.001777896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007628298,"about_ca_system_score_gemma":0.001309732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004406692,"about_ca_topic_score_gemma":0.003132756,"domain_scores_codex":[0.9994913,0.0002147299,0.00002712902,0.00005666671,0.0001763407,0.00003373426],"domain_scores_gemma":[0.9973614,0.001676562,0.0001288171,0.000299756,0.0004583824,0.00007526144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001995328,0.0001716215,0.001744971,0.0004667915,0.00005099602,0.001322783,0.0004268839,0.5615794,0.08730368,0.2343653,0.007561876,0.1048063],"study_design_scores_gemma":[0.00001782275,0.00004522414,0.0002166244,0.00003001794,0.00002521763,0.0007286621,0.00007940755,0.9480622,0.01501848,0.02547778,0.01025709,0.00004151603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003750567,0.0002230836,0.992142,0.0006575448,0.00004978199,0.00004376919,0.0000439485,0.0004982878,0.002591031],"genre_scores_gemma":[0.2005071,0.001406253,0.7863796,0.0003576474,0.0001392454,0.0002850674,0.0002208107,0.001178209,0.009526065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0050674,"threshold_uncertainty_score":0.01695216,"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."}}