{"id":"W2997004508","doi":"10.1016/j.jocn.2019.12.014","title":"An augmented reality system characterization of placement accuracy in neurosurgery","year":2019,"lang":"en","type":"article","venue":"Journal of Clinical Neuroscience","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Medicine; Neurosurgery; Characterization (materials science); Augmented reality; Medical physics; Artificial intelligence; Radiology; Nanotechnology; Computer science","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":[0.0006552971,0.0006111903,0.000566313,0.001439806,0.0002394304,0.001887788,0.0006545257,0.0009262366,0.002105957],"category_scores_gemma":[0.004785353,0.0004604562,0.000394036,0.000939656,0.0003209065,0.0006844658,0.0006943214,0.0004304639,0.0008123306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003094507,"about_ca_system_score_gemma":0.0005934726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002596553,"about_ca_topic_score_gemma":0.002427091,"domain_scores_codex":[0.9990699,0.0002397103,0.00009253869,0.0001209432,0.0004104777,0.00006638561],"domain_scores_gemma":[0.9976888,0.0008517723,0.000265273,0.0003581826,0.0007709897,0.00006505253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00562427,0.0004965913,0.04201121,0.0008735911,0.0004800013,0.0009081709,0.001576933,0.0880601,0.2388663,0.002373983,0.004283684,0.6144451],"study_design_scores_gemma":[0.0001396533,0.002680433,0.1254286,0.0001867506,0.0005598777,0.004186162,0.0006848567,0.7409367,0.1174967,0.001322245,0.006095045,0.000282932],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6980684,0.001349136,0.2916801,0.0001911849,0.0002100585,0.000155247,0.001407163,0.002587732,0.004351006],"genre_scores_gemma":[0.9653107,0.0003526285,0.03280391,0.00004961709,0.00002389753,0.00004386911,0.0004806079,0.0001151082,0.0008196512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002596553,"threshold_uncertainty_score":0.00704509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07987228460569906,"score_gpt":0.3955635623832153,"score_spread":0.3156912777775162,"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."}}