{"id":"W2998807817","doi":"10.1021/acsbiomaterials.9b00676","title":"3D Printing of Vascular Tubes Using Bioelastomer Prepolymers by Freeform Reversible Embedding","year":2020,"lang":"en","type":"article","venue":"ACS Biomaterials Science & Engineering","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Materials science; Polymer; Self-healing hydrogels; Polyester; Tissue engineering; Prepolymer; Composite material; Ultimate tensile strength; Biomedical engineering; Nanotechnology; Polymer chemistry; Polyurethane","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001145083,0.000272098,0.0003822999,0.0003492676,0.0001303477,0.0001616831,0.0008562941,0.0001163894,0.00008219079],"category_scores_gemma":[0.0007051154,0.0002729695,0.00007513749,0.001657834,0.0003367871,0.0006101044,0.0004394362,0.0001114711,0.00002230567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001595156,"about_ca_system_score_gemma":0.00008394202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005668476,"about_ca_topic_score_gemma":1.572031e-7,"domain_scores_codex":[0.9972151,0.00001809599,0.0005720319,0.0004480955,0.0008862618,0.0008604249],"domain_scores_gemma":[0.9990648,0.00006974414,0.00005875387,0.0003513921,0.00009056311,0.0003647517],"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":[0.000002300501,0.000006954342,0.0002475284,0.0002276797,0.00003060644,0.000003176571,0.0003130363,0.02529759,0.9733222,0.00003137913,0.00003693735,0.0004806292],"study_design_scores_gemma":[0.000138299,0.00002589641,0.0001248167,0.0001312683,0.00001200876,0.000005091257,0.00005437166,0.1850604,0.8136197,0.000001364636,0.0005898317,0.0002370506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786772,0.0002717213,0.01959823,0.00003242407,0.0006170625,0.0001998446,0.00001651328,0.0004612012,0.0001258032],"genre_scores_gemma":[0.9818246,0.00004605598,0.01791978,0.00001511912,0.0001228418,0.000008100386,0.000003560511,0.00005752234,0.000002387071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1597628,"threshold_uncertainty_score":0.9999722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01709500588273637,"score_gpt":0.2591305619966821,"score_spread":0.2420355561139457,"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."}}