{"id":"W3036003990","doi":"10.1109/jbhi.2020.3003643","title":"Tenodesis Grasp Detection in Egocentric Video","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Craig H. Neilsen Foundation","keywords":"GRASP; Computer science; Computer vision; Artificial intelligence; Human–computer interaction; Computer graphics (images)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002791095,0.000372648,0.0002189628,0.001060195,0.0001469696,0.0003361963,0.0002032238,0.0002991294,0.0008184766],"category_scores_gemma":[0.001560703,0.00009659708,0.0001471133,0.000620255,0.0001743811,0.0002452546,0.0003471267,0.0001358721,0.0002347076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003004367,"about_ca_system_score_gemma":0.0003694561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003732935,"about_ca_topic_score_gemma":0.007607,"domain_scores_codex":[0.9998097,0.00003531849,0.0000107501,0.00006303807,0.00005067092,0.0000306309],"domain_scores_gemma":[0.9995741,0.0001261783,0.000106635,0.00002700179,0.0001378682,0.00002822861],"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":[0.001445939,0.0002401438,0.07555196,0.0006094489,0.0001246402,0.0009947467,0.0007720012,0.0175911,0.362769,0.000903115,0.002855287,0.5361425],"study_design_scores_gemma":[0.00005593133,0.001214432,0.4951397,0.0002623609,0.0002082502,0.003711538,0.001231012,0.3202924,0.1662485,0.001935743,0.009602455,0.0000976601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8662276,0.0006158118,0.1284644,0.00008923567,0.00005604595,0.0001443782,0.0009969014,0.000543963,0.002861622],"genre_scores_gemma":[0.9432601,0.0003948597,0.05414287,0.00004843627,0.0000308823,0.00009267515,0.0007952864,0.00003180184,0.001203162],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003732935,"threshold_uncertainty_score":0.007422447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0352531382607448,"score_gpt":0.315603790082136,"score_spread":0.2803506518213912,"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."}}