{"id":"W4200201247","doi":"10.1109/embc46164.2021.9631100","title":"Application of 3D Printing Support Material for Neurosurgical Simulation","year":2021,"lang":"en","type":"article","venue":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Workflow; Material selection; Silicone; Computer science; 3D printing; Software deployment; Biomedical engineering; Manufacturing engineering; Materials science; Mechanical engineering; Engineering; Software engineering; Composite material; Database","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":[0.000849329,0.0004648691,0.0001949659,0.0005240988,0.0001590191,0.0005840476,0.0005049567,0.0005858777,0.003810864],"category_scores_gemma":[0.001995565,0.0002323284,0.0005642441,0.0002166692,0.0003066354,0.000385218,0.0005471183,0.0002804135,0.0008802506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001478406,"about_ca_system_score_gemma":0.0003230762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363147,"about_ca_topic_score_gemma":0.0002106825,"domain_scores_codex":[0.9993777,0.0001433775,0.000051092,0.00005168081,0.0003488187,0.00002742635],"domain_scores_gemma":[0.9989218,0.0005489889,0.0001136456,0.0001837647,0.0001786588,0.00005333397],"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":[0.0004315516,0.0001811885,0.003503618,0.0009374085,0.00003000914,0.001625245,0.0004766109,0.01229414,0.8141121,0.001045214,0.00128191,0.164081],"study_design_scores_gemma":[0.00007969325,0.00403833,0.0157793,0.0002391928,0.0001206014,0.009544991,0.0004115031,0.06066054,0.8606409,0.001533861,0.04679763,0.0001534693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6464096,0.002306058,0.3366207,0.0004736199,0.0002860246,0.0008281402,0.0005701362,0.001866795,0.01063892],"genre_scores_gemma":[0.7990206,0.001105445,0.1954863,0.0001188342,0.00004093088,0.0002728165,0.0002252523,0.0001704411,0.003559404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003810864,"threshold_uncertainty_score":0.0127486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02641847950700966,"score_gpt":0.3022039859571282,"score_spread":0.2757855064501185,"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."}}