{"id":"W3155228510","doi":"10.1109/jtehm.2021.3072347","title":"Identifying Hand Use and Hand Roles After Stroke Using Egocentric Video","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Translational Engineering in Health and Medicine","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Muscular Dystrophy Canada; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Hand held; Invisible hand; Stroke (engine); Physical medicine and rehabilitation; Computer science; Hand muscles; Artificial intelligence; Computer vision; Psychology; Human–computer interaction; Medicine; Neuroscience; Engineering","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":[0.0005455894,0.0004937986,0.0003227603,0.001131146,0.0001742045,0.0003879117,0.0002405621,0.0003921937,0.0008945637],"category_scores_gemma":[0.001961677,0.0001101724,0.0002737352,0.0005314607,0.00019325,0.0003120846,0.0003923227,0.0001994937,0.0003004418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002918344,"about_ca_system_score_gemma":0.0002755477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005717836,"about_ca_topic_score_gemma":0.01171896,"domain_scores_codex":[0.9996741,0.00006631277,0.00002053369,0.0001186472,0.00006283626,0.00005763382],"domain_scores_gemma":[0.9994857,0.0001710522,0.0001217527,0.00003847727,0.0001347367,0.00004822215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002030211,0.0006601631,0.4466179,0.0004778173,0.00031824,0.0006220657,0.001004572,0.01751411,0.07182201,0.0001972838,0.002206548,0.4565289],"study_design_scores_gemma":[0.00002598905,0.001016955,0.8668976,0.00008360725,0.0001370337,0.001010863,0.0005555784,0.1138606,0.01494372,0.0002661517,0.001149072,0.00005283977],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981594,0.0003342807,0.01603456,0.00002911731,0.00001421172,0.00009553556,0.0007757655,0.0001364879,0.0009861458],"genre_scores_gemma":[0.9899824,0.0002384177,0.008061181,0.00002011599,0.00001183875,0.00005908906,0.0008857753,0.000009835412,0.0007313204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005717836,"threshold_uncertainty_score":0.01136911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03379690641135734,"score_gpt":0.3111859275339532,"score_spread":0.2773890211225959,"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."}}