{"id":"W4400069019","doi":"10.1016/j.copbio.2024.103166","title":"Biofabrication strategies for cardiac tissue engineering","year":2024,"lang":"en","type":"review","venue":"Current Opinion in Biotechnology","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto General Hospital; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Biofabrication; Tissue engineering; Biochemical engineering; Computer science; Computational biology; Biomedical engineering; Biology; Medicine; Engineering","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":[0.000354309,0.0004200981,0.0009907322,0.001419275,0.00002119744,0.00007774441,0.0005968554,0.001175965,0.00001060506],"category_scores_gemma":[0.0001787657,0.0003856501,0.0002240909,0.001240611,0.00009775194,0.00005686311,0.0001961014,0.001409679,0.0002280692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003476169,"about_ca_system_score_gemma":0.0001602586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002496957,"about_ca_topic_score_gemma":2.099882e-7,"domain_scores_codex":[0.9981019,0.00002871476,0.0006361363,0.0004998507,0.0001805101,0.0005529229],"domain_scores_gemma":[0.9991727,0.0002062406,0.00005352275,0.0004730879,0.00002613268,0.00006825437],"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":[2.485235e-7,0.000009347836,1.16122e-7,0.07264928,0.00004878953,3.498652e-7,0.000009246222,0.00002380791,0.00001885991,0.007508743,0.004757807,0.9149734],"study_design_scores_gemma":[0.00004654713,0.00002525002,3.448352e-7,0.0150983,0.00005249647,0.000004092473,0.000007628569,0.002072439,0.00003518969,0.0003995074,0.9819275,0.0003307225],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.88183e-7,0.9720678,0.01208739,0.00007975486,0.01336553,0.001118284,0.0001251263,0.001117785,0.00003764181],"genre_scores_gemma":[0.000008263968,0.9969539,0.0009199566,2.691454e-7,0.0005898885,0.001124667,0.0002776801,0.0001143478,0.00001100766],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9771697,"threshold_uncertainty_score":0.9998595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09246149813749856,"score_gpt":0.4190166372716697,"score_spread":0.3265551391341711,"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."}}