{"id":"W4296288620","doi":"10.1016/j.media.2022.102611","title":"Anticipation for surgical workflow through instrument interaction and recognized Signals","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Computer science; Anticipation (artificial intelligence); Feature (linguistics); Workflow; Artificial intelligence; Task (project management); Segmentation; Noise (video); Inference; Laptop; Surgical instrument; Machine learning; Human–computer interaction; Computer vision; Pattern recognition (psychology)","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":[0.0007376099,0.0007682805,0.0003458874,0.0008331632,0.0003836654,0.001633067,0.0004739828,0.0007608776,0.003006038],"category_scores_gemma":[0.005567467,0.0004611999,0.0003785367,0.0004108339,0.0003629329,0.00145136,0.001209475,0.001297585,0.000929838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000325349,"about_ca_system_score_gemma":0.001142858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008357848,"about_ca_topic_score_gemma":0.001169836,"domain_scores_codex":[0.9995117,0.0001065361,0.00002248949,0.00009895216,0.0001904951,0.00006974501],"domain_scores_gemma":[0.998582,0.000803218,0.0002032368,0.00007545861,0.0002133473,0.0001228216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003051229,0.0006384011,0.02519031,0.0004231276,0.0001121881,0.001029258,0.001011038,0.09825839,0.3401305,0.01180221,0.005876273,0.5124772],"study_design_scores_gemma":[0.00006188007,0.001059921,0.03356269,0.0001205738,0.0001082024,0.001001229,0.0003993028,0.869897,0.07847875,0.009705706,0.00546002,0.0001447767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1355085,0.0004108865,0.8548627,0.0006001445,0.0003417999,0.0001122011,0.0001589945,0.001621587,0.006383173],"genre_scores_gemma":[0.8830636,0.0003238539,0.1134097,0.0001593126,0.0001078471,0.00006521754,0.0001462386,0.0001868068,0.00253737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003006038,"threshold_uncertainty_score":0.0100562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0558351727496109,"score_gpt":0.3775551914670036,"score_spread":0.3217200187173927,"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."}}