{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003121166,0.0001454448,0.0003269919,0.00008174788,0.00002054847,0.000003928158,0.0003313449,0.0002490153,0.0001745204],"category_scores_gemma":[0.0004297129,0.0001212581,0.0001124041,0.0002549608,0.0001864997,0.00005424082,0.00007549903,0.0002745579,0.000001712664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000398749,"about_ca_system_score_gemma":0.00004101467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001757122,"about_ca_topic_score_gemma":0.00001105073,"domain_scores_codex":[0.9989251,0.00002362494,0.0005014053,0.0002080302,0.0001453896,0.0001964256],"domain_scores_gemma":[0.9991319,0.0002318889,0.0001104173,0.0002054057,0.0002843816,0.00003606781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001016355,0.000216869,0.008123297,0.0007978102,0.000591327,0.000003840833,0.002455009,0.326776,0.6069632,0.02919583,0.004845373,0.01992986],"study_design_scores_gemma":[0.001619338,0.0001116964,0.002812522,0.0002781223,0.00005765956,0.00001991337,0.0003048415,0.8246143,0.02630443,0.001218628,0.1423448,0.0003137126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5958771,0.0001262189,0.3978812,0.001568721,0.003375893,0.0003557069,0.0001617478,0.0001186441,0.0005349066],"genre_scores_gemma":[0.9949629,0.0002609111,0.004090744,0.00006094568,0.0003061664,0.0000368853,0.0001565187,0.00001631319,0.0001085592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5806587,"threshold_uncertainty_score":0.4944761,"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."}}