{"id":"W4286267536","doi":"10.1016/j.cell.2022.06.015","title":"Tissue bioprinting for biology and medicine","year":2022,"lang":"en","type":"article","venue":"Cell","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of General Medical Sciences","keywords":"Biology; 3D bioprinting; Regenerative medicine; Tissue engineering; Computational biology; Engineering ethics; Stem cell; Cell biology; Engineering; Genetics","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":[0.0006364887,0.0006808018,0.0007453677,0.001334449,0.0004931568,0.001839196,0.0006588321,0.001706985,0.006400215],"category_scores_gemma":[0.0006076402,0.0004264436,0.000479374,0.001106407,0.001337535,0.001346467,0.001074041,0.001551829,0.003685672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008174096,"about_ca_system_score_gemma":0.0003397082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004145925,"about_ca_topic_score_gemma":0.0006912746,"domain_scores_codex":[0.9994953,0.00006583542,0.00003294841,0.00008596684,0.0002844777,0.00003543952],"domain_scores_gemma":[0.9996451,0.0001597131,0.00004261253,0.00005763715,0.00006930389,0.00002548553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000951229,0.00008541624,0.0002813292,0.001754651,0.00003634621,0.0005887049,0.0004457082,0.001658646,0.549735,0.1635167,0.01326049,0.2685419],"study_design_scores_gemma":[0.00002067412,0.0001846805,0.001135685,0.0002769006,0.00005700744,0.002845292,0.0001418798,0.006291878,0.5667619,0.04108207,0.3811281,0.00007396769],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.06300746,0.3337882,0.3861575,0.007860857,0.006822584,0.0002184674,0.000759556,0.00186772,0.1995176],"genre_scores_gemma":[0.3759154,0.185202,0.2100332,0.003237035,0.003043187,0.0003621085,0.0007833568,0.0008931247,0.2205305],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006400215,"threshold_uncertainty_score":0.02141088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02311367427880892,"score_gpt":0.3188549089968623,"score_spread":0.2957412347180534,"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."}}