{"id":"W2887928253","doi":"10.1016/j.biomaterials.2018.08.006","title":"Multiscale bioprinting of vascularized models","year":2018,"lang":"en","type":"review","venue":"Biomaterials","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":263,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health; National Cancer Institute; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research","keywords":"3D bioprinting; Regeneration (biology); Tissue engineering; Angiogenesis; Biomedical engineering; Materials science; Blood vessel; Computer science; Biology; Cell biology; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001265921,0.0004056931,0.001893314,0.0003425342,0.00003555731,0.00007016613,0.0007272248,0.0006012177,0.0003426609],"category_scores_gemma":[0.0002269868,0.0003278347,0.0004672176,0.0003729829,0.0002136893,0.00006721821,0.0003530561,0.00004607747,0.0004239446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007915816,"about_ca_system_score_gemma":0.00007366192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002630415,"about_ca_topic_score_gemma":4.71756e-7,"domain_scores_codex":[0.9973616,0.0001793965,0.001160411,0.0003619379,0.0004407616,0.0004958771],"domain_scores_gemma":[0.998674,0.0001867741,0.0002022457,0.0007167113,0.00007976191,0.0001404598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003800925,0.00004492714,3.765999e-7,0.1144883,0.0008046553,0.00001291367,0.00004443191,0.000005889127,0.01801309,0.0002102448,0.001028234,0.8653432],"study_design_scores_gemma":[0.0002140505,0.00002205865,0.000001138116,0.01594854,0.0002420168,0.00001584264,0.000001808179,0.0008702059,0.01227583,0.0002424127,0.969651,0.0005151187],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003551493,0.9943051,0.001908244,0.0000011769,0.001314177,0.000671379,0.0001154594,0.0003979795,0.0009313549],"genre_scores_gemma":[0.0002171226,0.9938903,0.005107548,9.452629e-7,0.0004130811,0.0000956518,0.00006913281,0.0001387189,0.00006753526],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9686227,"threshold_uncertainty_score":0.9999174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1136053727620022,"score_gpt":0.3529720848012151,"score_spread":0.2393667120392129,"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."}}