{"id":"W4395465573","doi":"10.3390/jpm14050450","title":"Evaluation of a 3D Printed Silicone Oral Cavity Cancer Model for Surgical Simulations","year":2024,"lang":"en","type":"article","venue":"Journal of Personalized Medicine","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Medicine; Silicone; Tongue; Surgical margin; 3d printed; Otorhinolaryngology; Cancer; Surgery; Biomedical engineering; Internal medicine; Pathology","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":[0.001619919,0.0004917849,0.0002500985,0.0005113956,0.0001416169,0.0004943598,0.0005875116,0.0005020081,0.002588585],"category_scores_gemma":[0.003608731,0.0002877827,0.0005971959,0.0002455726,0.0003499967,0.0002754127,0.0006394967,0.000295683,0.0003321198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002682871,"about_ca_system_score_gemma":0.00059055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006035986,"about_ca_topic_score_gemma":0.0007656523,"domain_scores_codex":[0.9992059,0.0002479744,0.00006048548,0.00006382864,0.0003609745,0.00006087598],"domain_scores_gemma":[0.9980356,0.001114566,0.0001531492,0.0002474646,0.0003042941,0.0001448738],"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":[0.002798108,0.002441979,0.01419382,0.001050874,0.0001124086,0.001224694,0.001941672,0.1616782,0.6989856,0.001381061,0.001284407,0.1129072],"study_design_scores_gemma":[0.0003915092,0.03584605,0.04440799,0.000325455,0.0003861654,0.003312991,0.001603531,0.4434349,0.4474463,0.00110447,0.02141103,0.0003296826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9614231,0.000207171,0.03583561,0.0000838488,0.0001046122,0.0003351405,0.0002338354,0.0001363575,0.001640299],"genre_scores_gemma":[0.9576069,0.0003119087,0.04034119,0.00004412965,0.00001503119,0.00024463,0.0002537096,0.00003970894,0.00114277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002588585,"threshold_uncertainty_score":0.008659601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.189340367204821,"score_gpt":0.4672184067950371,"score_spread":0.2778780395902161,"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."}}