{"id":"W4408443825","doi":"10.54808/imcic2025.01.112","title":"Quantitative Endosurgery Process Analysis by Machine Learning Method","year":2025,"lang":"en","type":"article","venue":"Proceedings","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Process (computing); Artificial intelligence; Machine learning; Programming language","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":[0.0004535361,0.0001638688,0.0003824352,0.0004862498,0.00009500845,0.00008054459,0.0001466614,0.00005804675,0.0001050277],"category_scores_gemma":[0.000394584,0.0001500541,0.0001600961,0.002539972,0.00003141404,0.0001487217,0.0000223584,0.0003281332,0.00001468421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003223858,"about_ca_system_score_gemma":0.0000099387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004219152,"about_ca_topic_score_gemma":9.386578e-7,"domain_scores_codex":[0.9990059,0.00001043222,0.0002573286,0.000254862,0.0002073302,0.0002641547],"domain_scores_gemma":[0.9995632,0.0001302321,0.00004509655,0.00005369788,0.0001187795,0.00008906382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005596036,0.0003029722,0.6063513,0.003528473,0.02015671,0.00001970437,0.01026532,0.04988067,0.1052768,0.006288519,0.06998734,0.1278862],"study_design_scores_gemma":[0.0001365875,0.000008245209,0.0002416378,0.00004997264,0.0009372701,7.499457e-7,0.0004791809,0.9707059,0.01062182,0.0003100431,0.01630669,0.0002018675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2188289,0.006826278,0.6913668,0.002043388,0.0002476611,0.000210648,0.00002456975,0.002244939,0.07820686],"genre_scores_gemma":[0.9908125,0.0001368194,0.006369417,0.000177066,0.00002209612,0.00003548302,0.00003593815,0.00002139489,0.002389268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9208252,"threshold_uncertainty_score":0.6119031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007319970449672697,"score_gpt":0.2903313112537253,"score_spread":0.2830113408040526,"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."}}