{"id":"W3088679822","doi":"10.1039/d0ra07308c","title":"MRI contrast enhancement of liver pre-neoplasia using iron–tannic nanoparticles","year":2020,"lang":"en","type":"article","venue":"RSC Advances","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Associated Medical Services","funders":"Faculty of Science, Chiang Mai University; National Research Council of Thailand; Materials Science Research Center, Faculty of Science, Chiang Mai University; Chiang Mai University","keywords":"Tannic acid; Contrast (vision); Liver cancer; Nanoparticle; Stage (stratigraphy); Chemistry; Radiology; Cancer; Materials science; Nanotechnology; Medicine; Computer science; Internal medicine; Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001287022,0.000259074,0.000114609,0.0001553825,0.00006585522,0.0000912355,0.00009841413,0.0002410477,0.0007646843],"category_scores_gemma":[0.0001243141,0.0001006676,0.000120128,0.00006306715,0.000136718,0.0001489376,0.00009817829,0.0002061518,0.000166549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032889,"about_ca_system_score_gemma":0.00007449284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004249861,"about_ca_topic_score_gemma":0.0006400824,"domain_scores_codex":[0.9999586,0.00001205002,0.000002249801,0.00001070774,0.000007842564,0.000008528202],"domain_scores_gemma":[0.9999387,0.00002059023,0.00001689229,0.000004420455,0.00001146404,0.000007835515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005243793,0.00000594282,0.0000968739,0.00002966172,0.000002239949,0.00004134355,0.000008820582,0.00004709646,0.9989339,0.00003251774,0.00001526748,0.0007339426],"study_design_scores_gemma":[0.000002747474,0.0002061894,0.00114797,0.000002719187,0.000009294611,0.0001768022,0.000006460843,0.0007833139,0.9970824,0.00001460716,0.0005658166,0.000001702282],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793676,0.002618386,0.01456141,0.0001089814,0.00002528157,0.00003866835,0.000064659,0.0001206337,0.003094386],"genre_scores_gemma":[0.9879234,0.001105336,0.008115213,0.00005089682,0.00001039139,0.00001674056,0.00008829831,0.00001296815,0.002676782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007646843,"threshold_uncertainty_score":0.002558172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02125732120454796,"score_gpt":0.2840135244289054,"score_spread":0.2627562032243574,"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."}}