{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001509639,0.00006697937,0.0001561198,0.00008805795,0.0000490154,0.00001646525,0.00004171125,0.00005679569,0.00004123086],"category_scores_gemma":[0.0002571349,0.00006270788,0.00005085961,0.0006606404,0.000009622944,0.00004313091,0.00002027815,0.000109808,0.00001391137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002152502,"about_ca_system_score_gemma":0.000127341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002057805,"about_ca_topic_score_gemma":0.0000620268,"domain_scores_codex":[0.9991989,0.00001585695,0.0002597148,0.0002153844,0.0001303398,0.0001798476],"domain_scores_gemma":[0.9992735,0.0002649716,0.00003632073,0.000186696,0.0001488045,0.00008974148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003082857,0.007365122,0.4133081,0.0005441629,0.000177207,0.00001112509,0.001571059,0.00495243,0.179675,0.002633869,0.000581249,0.3888723],"study_design_scores_gemma":[0.001103892,0.0009497807,0.9214967,0.00003694886,0.00004706707,0.00001761669,0.00008817763,0.02853972,0.04093382,0.0001576079,0.006498909,0.0001297484],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6574249,0.000006198561,0.3407367,0.0005043212,0.0001401573,0.0004636399,0.000005763561,0.00003508925,0.0006832989],"genre_scores_gemma":[0.9336956,0.00003713114,0.06486412,0.0007724083,0.00009904761,0.0001171645,0.00008421509,0.00001123568,0.0003191489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5081886,"threshold_uncertainty_score":0.2557153,"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."}}