{"id":"W3131157270","doi":"10.1117/12.2581889","title":"Object detection to compute performance metrics for skill assessment in central venous catheterization","year":2021,"lang":"en","type":"article","venue":"","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Artificial neural network; Object detection; Path (computing); Object (grammar); Psychological intervention; Reduction (mathematics); Pattern recognition (psychology); Medicine; Computer network","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":[0.001510985,0.001024935,0.0005599654,0.001783693,0.0002007383,0.0007600261,0.0006690048,0.0009090495,0.0009133831],"category_scores_gemma":[0.006113717,0.000331315,0.0004491551,0.0009032192,0.0002992735,0.0007535421,0.0006467325,0.0006007407,0.0004357923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001074426,"about_ca_system_score_gemma":0.0007527468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007433786,"about_ca_topic_score_gemma":0.007183493,"domain_scores_codex":[0.9992267,0.0001023713,0.00004741252,0.0003019294,0.0002426837,0.00007899106],"domain_scores_gemma":[0.9977418,0.001061354,0.0004391715,0.0001760424,0.0005161939,0.00006546438],"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":[0.0008885928,0.0003838953,0.04620168,0.0002761135,0.0002650735,0.0001670124,0.0001812056,0.1252457,0.07562456,0.0005960271,0.001752548,0.7484177],"study_design_scores_gemma":[0.00002186666,0.0003355632,0.03804618,0.00004348086,0.00007345984,0.0001851383,0.0000416952,0.925986,0.0338266,0.0006722997,0.0007319916,0.00003569037],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4492031,0.00137656,0.5431954,0.000165756,0.0001241082,0.0002182332,0.0005682779,0.002922746,0.002225709],"genre_scores_gemma":[0.8765314,0.0002912029,0.121219,0.00008698808,0.00002619107,0.0001049197,0.0005184695,0.00008576026,0.001136118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007433786,"threshold_uncertainty_score":0.014781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03288181654923119,"score_gpt":0.3563178793688318,"score_spread":0.3234360628196006,"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."}}