{"id":"W2804064570","doi":"10.1038/s41467-018-04336-z","title":"Fibrotic microtissue array to predict anti-fibrosis drug efficacy","year":2018,"lang":"en","type":"article","venue":"Nature Communications","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; University at Buffalo; National Institutes of Health","keywords":"Nintedanib; Fibrosis; Medicine; Pirfenidone; Pulmonary fibrosis; Idiopathic pulmonary fibrosis; Pathology; Pharmacology; Lung; 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":[0.000399191,0.0003040533,0.0003336389,0.0006427412,0.00008651969,0.0003506814,0.0001577093,0.0005663569,0.001306708],"category_scores_gemma":[0.0006803757,0.0001794583,0.0001883007,0.0003280565,0.0001480971,0.0002736796,0.0001511989,0.0003131629,0.0004300847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000173513,"about_ca_system_score_gemma":0.0001362965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003939241,"about_ca_topic_score_gemma":0.0008443631,"domain_scores_codex":[0.9997272,0.00005312397,0.00001875864,0.00006163899,0.0001214258,0.00001788191],"domain_scores_gemma":[0.9996742,0.0001577599,0.00006514178,0.0000306847,0.00005214052,0.00001995896],"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":[0.0003452724,0.0001183668,0.007988482,0.000110895,0.00003675712,0.0001026696,0.00003480168,0.02070908,0.9432943,0.0002183639,0.0002681783,0.0267729],"study_design_scores_gemma":[0.00002192009,0.0008745411,0.02087065,0.00001891305,0.00009622306,0.0004122939,0.00005727981,0.2571879,0.7168239,0.0004199785,0.00318652,0.00002980521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7816162,0.005004781,0.2032364,0.0002884527,0.0001250098,0.0002145079,0.001980633,0.001225111,0.006308829],"genre_scores_gemma":[0.9361674,0.001170042,0.06055915,0.0001941638,0.00002286091,0.0001950958,0.0004775188,0.00003283355,0.001181037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001306708,"threshold_uncertainty_score":0.004371405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01683263364360364,"score_gpt":0.3171981882555788,"score_spread":0.3003655546119752,"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."}}