{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007901625,0.001264285,0.001443078,0.002398071,0.0002282883,0.001296761,0.0008659637,0.001225897,0.001875607],"category_scores_gemma":[0.000806643,0.0006061796,0.0005705386,0.002172119,0.0006123413,0.001359403,0.0009576282,0.001350717,0.001225514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005105038,"about_ca_system_score_gemma":0.0006234061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005556023,"about_ca_topic_score_gemma":0.001144543,"domain_scores_codex":[0.9997397,0.00002986207,0.00003308201,0.00005303392,0.0001172232,0.00002693332],"domain_scores_gemma":[0.9996579,0.0001880329,0.00005864182,0.00001900855,0.00005736649,0.00001913368],"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":[0.00005281473,0.00007235612,0.00009616265,0.01741013,0.0000707776,0.0001876592,0.00006317895,0.0009587991,0.01531319,0.006729783,0.009210817,0.9498344],"study_design_scores_gemma":[0.00001780893,0.0001012921,0.000709633,0.003030872,0.0001577064,0.001414621,0.00005270347,0.0007995414,0.01485032,0.003116773,0.9757,0.00004875939],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003307973,0.9973334,0.001031343,0.00008664783,0.0001678012,0.000005514737,0.00001405409,0.00001227787,0.001018259],"genre_scores_gemma":[0.001975718,0.9957795,0.0009020016,0.00008425503,0.0001440395,0.000009322763,0.00003114762,0.00000487736,0.001069044],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002398071,"threshold_uncertainty_score":0.006274521,"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."}}