{"id":"W4285187007","doi":"10.1109/lra.2022.3186769","title":"A Domain-Adapted Machine Learning Approach for Visual Evaluation and Interpretation of Robot-Assisted Surgery Skills","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation; Government of Alberta","keywords":"Artificial intelligence; Domain (mathematical analysis); Computer science; Notation; Machine learning; Set (abstract data type); Smoothness; Algorithm; Mathematics; Arithmetic; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008169934,0.00009738657,0.0002478121,0.0001905473,0.0001763,0.00002161652,0.00001827314,0.00003300828,0.00002308786],"category_scores_gemma":[0.0001004363,0.00009500004,0.00006775826,0.000180886,0.00003708064,0.00006275869,0.00001571454,0.0001151126,1.755596e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005168323,"about_ca_system_score_gemma":0.00002852402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005371758,"about_ca_topic_score_gemma":4.824379e-7,"domain_scores_codex":[0.9988984,0.0001576204,0.000328385,0.000183485,0.000320403,0.0001117237],"domain_scores_gemma":[0.9992306,0.0003750735,0.000206359,0.0000556762,0.00007870736,0.00005354233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002835871,0.0002143759,0.008769519,0.0001936033,0.0001231802,0.000002368189,0.001394099,0.8449867,0.009252449,0.0002853297,0.00005015743,0.1344447],"study_design_scores_gemma":[0.002136445,0.0001075051,0.03263029,0.00003201775,0.0001052371,0.00001967238,0.0002045652,0.9643589,0.000110876,0.00002810623,0.0001724056,0.00009394576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7255372,0.00007111819,0.27279,0.0008770958,0.0001050429,0.0004812263,0.000004970105,0.00005351582,0.00007978465],"genre_scores_gemma":[0.9873272,0.000004762327,0.01174641,0.0004729525,0.00003219413,0.0000523723,0.0003358447,0.00001512858,0.00001316901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2617899,"threshold_uncertainty_score":0.3873989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02746927689576602,"score_gpt":0.2989143659457146,"score_spread":0.2714450890499486,"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."}}