{"id":"W4319944766","doi":"10.1016/j.apmt.2023.101737","title":"Tissue engineering of skeletal muscle, tendons and nerves: A review of manufacturing strategies to meet structural and functional requirements","year":2023,"lang":"en","type":"review","venue":"Applied Materials Today","topic":"Electrospun Nanofibers in Biomedical Applications","field":"Materials Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek; European Cooperation in Science and Technology","keywords":"Macro; Tissue engineering; Computer science; Fabrication; Function (biology); Nanotechnology; Biochemical engineering; Engineering; Materials science; Biomedical engineering; Biology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006851958,0.0004482412,0.001802588,0.000196099,0.00007204467,0.00007076204,0.0003094307,0.0002025064,0.0002795472],"category_scores_gemma":[0.0000382353,0.000360822,0.00007061601,0.0002778273,0.0001785442,0.0001023276,0.0004047282,0.00008041067,0.00002214781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005014475,"about_ca_system_score_gemma":0.0001171684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003000908,"about_ca_topic_score_gemma":0.000003416263,"domain_scores_codex":[0.9972545,0.00005993518,0.001279923,0.0006074849,0.0004180544,0.0003801186],"domain_scores_gemma":[0.9985548,0.0002142889,0.0006086511,0.0004309614,0.00004638517,0.0001448961],"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":[0.000006726958,0.00001941376,5.489017e-8,0.2091288,0.0001165635,0.000003118886,0.00004671337,0.000001270662,0.7165086,0.01224856,0.0007337664,0.06118639],"study_design_scores_gemma":[0.0004758722,0.0002059705,0.000101107,0.08291248,0.00133636,0.00007400005,0.00003942362,3.991034e-7,0.5998213,0.002884578,0.3108196,0.001328995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005263019,0.9898626,0.0004317846,0.0000662918,0.0005219943,0.002782711,0.0007611268,0.0001591288,0.0001513891],"genre_scores_gemma":[0.00110356,0.9908925,0.006842725,0.00003394455,0.0001664556,0.0006528358,0.0001929064,0.00007898541,0.0000361458],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.3100858,"threshold_uncertainty_score":0.9998844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02958683489720829,"score_gpt":0.3103994823629805,"score_spread":0.2808126474657722,"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."}}