{"id":"W2982892418","doi":"10.1142/s2424905x19420017","title":"Augmented Reality Training Platform for Neurosurgical Burr Hole Localization","year":2019,"lang":"en","type":"article","venue":"Journal of Medical Robotics Research","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Southeastern Ontario Academic Medical Organization; Canada Research Chairs","keywords":"Drill; Augmented reality; Computer science; Position (finance); Neurosurgery; Training (meteorology); Identification (biology); Plan (archaeology); Medical physics; Work (physics); Curriculum; Artificial intelligence; Simulation; Medicine; Surgery; Engineering; Psychology; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004981214,0.0007862771,0.0004243394,0.0006775916,0.0001930196,0.0007908281,0.0007798356,0.0006569586,0.01095914],"category_scores_gemma":[0.00164034,0.0003370036,0.0004548786,0.0002716286,0.0002121198,0.0005656295,0.001328809,0.0006002892,0.002584699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002304313,"about_ca_system_score_gemma":0.0007356156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008510031,"about_ca_topic_score_gemma":0.001001875,"domain_scores_codex":[0.9994346,0.0001293056,0.00003366865,0.00007767134,0.000270171,0.00005439067],"domain_scores_gemma":[0.9994017,0.000152083,0.00006357297,0.0001005833,0.0001976114,0.00008433158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001665154,0.001329799,0.00588446,0.0008293742,0.0001370917,0.0005768515,0.0007954161,0.05145886,0.159966,0.004496688,0.01506115,0.7577991],"study_design_scores_gemma":[0.0004246442,0.009209933,0.04665529,0.0006342823,0.0003556969,0.003925754,0.0008832311,0.6080148,0.2084095,0.006134828,0.1147851,0.0005669075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1546209,0.001030648,0.8162678,0.0004456362,0.0004172951,0.0009452481,0.001607562,0.01066088,0.01400408],"genre_scores_gemma":[0.67906,0.0007763813,0.3059701,0.0002055115,0.00007198169,0.0009671597,0.001538583,0.0002871639,0.01112322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01095914,"threshold_uncertainty_score":0.03666198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2926050359993079,"score_gpt":0.4844296492808533,"score_spread":0.1918246132815454,"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."}}