{"id":"W2956525133","doi":"10.3390/mi10070480","title":"The Applications of 3D Printing for Craniofacial Tissue Engineering","year":2019,"lang":"en","type":"review","venue":"Micromachines","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scaffold; 3D printing; Tissue engineering; Selective laser sintering; Biomedical engineering; Materials science; Three dimensional printing; Rapid prototyping; Craniofacial; Dental alveolus; Stereolithography; 3d printed; Dentistry; Engineering; Medicine; Sintering; Composite material","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":[0.0005577325,0.0007953872,0.0008569905,0.00355114,0.0002817312,0.0009885682,0.0005246446,0.001058735,0.003667263],"category_scores_gemma":[0.0004809829,0.0003633117,0.0008889092,0.002149433,0.0005164574,0.0009598447,0.0006897391,0.001509846,0.001585168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004920046,"about_ca_system_score_gemma":0.0006269741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008580262,"about_ca_topic_score_gemma":0.001161205,"domain_scores_codex":[0.9996786,0.00004007947,0.00003722911,0.00005274148,0.0001669163,0.00002446849],"domain_scores_gemma":[0.9997705,0.0001322746,0.0000298629,0.000009768112,0.00004670633,0.00001091253],"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":[0.00003038891,0.00007623353,0.000146697,0.01413074,0.00007939425,0.0003528477,0.00009777494,0.0009853472,0.01708905,0.009487474,0.009502312,0.9480218],"study_design_scores_gemma":[0.000009528808,0.0001125363,0.0008058597,0.002081813,0.00007374497,0.002285797,0.00005936508,0.0003713362,0.008666353,0.003034803,0.9824589,0.00003976744],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003771097,0.9951958,0.001055507,0.0001538135,0.0001849111,0.000009631028,0.0000209863,0.00001852612,0.002983705],"genre_scores_gemma":[0.002399486,0.9949558,0.001021901,0.0001314364,0.0001251189,0.00001259394,0.00003401178,0.000003731583,0.001315868],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003667263,"threshold_uncertainty_score":0.01226825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0278090054191582,"score_gpt":0.3440090114670218,"score_spread":0.3162000060478636,"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."}}