{"id":"W4387540716","doi":"10.1371/journal.pdig.0000361","title":"Recognizing hand use and hand role at home after stroke from egocentric video","year":2023,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Artificial intelligence; Computer science; Wearable computer; Random forest; Stroke (engine); Context (archaeology); Physical medicine and rehabilitation; Psychology; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008083406,0.000148111,0.0003407685,0.0002209402,0.0001515309,0.0002305938,0.00003206682,0.00007528396,0.00008595712],"category_scores_gemma":[0.0002939634,0.0001236165,0.00008490337,0.0002468228,0.0001006077,0.0003790921,0.00007510557,0.0001320396,0.000340895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001888528,"about_ca_system_score_gemma":0.0001130777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009758113,"about_ca_topic_score_gemma":0.00006277931,"domain_scores_codex":[0.9986839,0.00002931541,0.0002993233,0.0003420728,0.0002747717,0.0003705594],"domain_scores_gemma":[0.9987542,0.0005247428,0.00007190643,0.0001915929,0.00006381419,0.0003937567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005008446,0.0001607109,0.9637738,0.0003036358,0.0000908631,0.00003999422,0.001388783,7.035684e-7,0.0007265403,0.000003151175,0.002167375,0.0308436],"study_design_scores_gemma":[0.00190302,0.0004786454,0.9844432,0.0004183229,0.00003244267,0.00001890381,0.0004688949,0.0003129884,0.0005705718,0.0001726791,0.01101151,0.0001687644],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948853,0.002231589,0.00001024639,0.001303941,0.0001918615,0.0004111764,0.000570862,0.0001131957,0.0002818505],"genre_scores_gemma":[0.9939894,0.0005935911,0.0001288584,0.0007049974,0.0001594612,0.00002483172,0.0002095242,0.00003289418,0.004156408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03067484,"threshold_uncertainty_score":0.5040933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02897634744824217,"score_gpt":0.2660009039011955,"score_spread":0.2370245564529533,"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."}}