{"id":"W4249254522","doi":"10.31234/osf.io/5eq7w","title":"Assessing the efficacy of tablet-based simulations for learning pseudo-surgical instrumentation","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Undo; Computer science; Identification (biology); Instrumentation (computer programming); Machine learning; Task (project management); Haptic technology; Artificial intelligence; Human–computer interaction; Medical physics; Medicine; Engineering","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":[0.001277884,0.0006865485,0.0003803732,0.0003749861,0.0001593726,0.0007935759,0.0006970994,0.0007821237,0.005067544],"category_scores_gemma":[0.01500983,0.0002772139,0.0003760433,0.0001711263,0.0003590846,0.0006473961,0.0008737309,0.0004150366,0.0006278567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002862241,"about_ca_system_score_gemma":0.0004319306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005080468,"about_ca_topic_score_gemma":0.000493449,"domain_scores_codex":[0.9987327,0.0005335621,0.0001860476,0.0001461455,0.0002986654,0.0001027428],"domain_scores_gemma":[0.9891256,0.007953905,0.001157743,0.0007015488,0.000463764,0.0005974362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.05824918,0.05974674,0.07563473,0.003479875,0.0007491033,0.0003305736,0.003190745,0.1076831,0.1529649,0.001682109,0.00252229,0.5337667],"study_design_scores_gemma":[0.005749151,0.2814113,0.2335749,0.000935482,0.0009542512,0.0007064605,0.001360209,0.2394436,0.2217236,0.002774713,0.01092374,0.0004426194],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921033,0.0001030446,0.005856714,0.00005084428,0.00002382983,0.0004511719,0.0001601282,0.00009975308,0.001151372],"genre_scores_gemma":[0.9807984,0.00022952,0.01643941,0.00004412216,0.00001944461,0.0008341628,0.0002822099,0.00002340179,0.001329415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005067544,"threshold_uncertainty_score":0.01695269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026735133294118,"score_gpt":0.4095304858079327,"score_spread":0.3068569724785209,"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."}}