{"id":"W2921884208","doi":"10.1117/12.2513053","title":"Evaluation of 3D slicer as a medical virtual reality visualization platform","year":2019,"lang":"en","type":"article","venue":"","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Visualization; Virtual reality; Computer science; Human–computer interaction; Computer graphics (images); Augmented reality; Data visualization; Artificial intelligence","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":[0.003752173,0.0009222316,0.0003961094,0.0004949418,0.0001879615,0.0008821136,0.001073167,0.0005584669,0.002705945],"category_scores_gemma":[0.00974091,0.0002736575,0.0006312077,0.0001836952,0.0004321165,0.0008787919,0.001330964,0.00045942,0.0005458411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002362579,"about_ca_system_score_gemma":0.0005253705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006542332,"about_ca_topic_score_gemma":0.0006843644,"domain_scores_codex":[0.9975164,0.001358449,0.0001496857,0.0002014362,0.0006432622,0.0001306649],"domain_scores_gemma":[0.9943622,0.003145108,0.0003689403,0.0004792121,0.001061211,0.000583312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01098895,0.004946725,0.02154341,0.006859827,0.0006661095,0.002203006,0.006952518,0.02918155,0.497532,0.00227292,0.006145349,0.4107077],"study_design_scores_gemma":[0.003811128,0.1125824,0.2412082,0.00217165,0.00200821,0.009131769,0.004366063,0.2149228,0.3211886,0.001925082,0.08573024,0.0009539463],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9315082,0.0008200359,0.06089081,0.0001962513,0.0001321679,0.0009896716,0.0004287173,0.002086002,0.002948073],"genre_scores_gemma":[0.8745623,0.0006685288,0.1207724,0.0001588504,0.00005880363,0.0006265115,0.001058635,0.000460112,0.001633807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003752173,"threshold_uncertainty_score":0.01984364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09766763131236754,"score_gpt":0.422845870650652,"score_spread":0.3251782393382844,"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."}}