{"id":"W3210133259","doi":"10.1109/tbme.2021.3124422","title":"System for Central Venous Catheterization Training Using Computer Vision-Based Workflow Feedback","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; California HIV/AIDS Research Program","keywords":"Workflow; Usability; Computer science; Training system; Convolutional neural network; Observer (physics); Task (project management); Multimedia; Artificial intelligence; Human–computer interaction; Database","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":[0.001582529,0.0008083678,0.0003770074,0.000713612,0.0002940931,0.0004637889,0.001205434,0.00075028,0.007704227],"category_scores_gemma":[0.003911036,0.0002218327,0.0004184022,0.0001970756,0.0001545033,0.0005500661,0.0008047018,0.0005710511,0.001753929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004603309,"about_ca_system_score_gemma":0.001035423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00199358,"about_ca_topic_score_gemma":0.00186722,"domain_scores_codex":[0.9992518,0.0001593386,0.00007490811,0.0001717626,0.0002712509,0.00007094477],"domain_scores_gemma":[0.9982218,0.0004645403,0.0001584947,0.0001317248,0.0007826252,0.0002407046],"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":[0.00172303,0.001493943,0.007223905,0.0005398736,0.00007008672,0.000599537,0.000467377,0.00832256,0.1531759,0.0005202637,0.01550432,0.8103591],"study_design_scores_gemma":[0.00102709,0.005806475,0.04048207,0.0004465639,0.0003208554,0.00342913,0.0003441554,0.6327974,0.2647681,0.002213676,0.0479821,0.0003824701],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1065039,0.0004790509,0.8516312,0.0007505289,0.0004537828,0.001170748,0.0005989987,0.03482165,0.003590241],"genre_scores_gemma":[0.4941823,0.000321621,0.4962428,0.0005641911,0.0001482219,0.0008614956,0.0008354512,0.0004245752,0.006419323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007704227,"threshold_uncertainty_score":0.02577323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02650022912563249,"score_gpt":0.2648151116767138,"score_spread":0.2383148825510814,"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."}}