{"id":"W4319334338","doi":"10.1101/2023.01.25.23284790","title":"Wearable technology to capture arm use of stroke survivors in home and community settings: feasibility and early insights on motor performance","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Institutes of Health; Southern California Clinical and Translational Science Institute; National Center for Advancing Translational Sciences; Small Business Technology Transfer; University of Southern California","keywords":"Wearable computer; Physical medicine and rehabilitation; Stroke (engine); Psychology; Medicine; Computer science; Engineering; Embedded system","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":[0.0004063639,0.0004160239,0.0002452263,0.0004731536,0.0001472583,0.0002719528,0.0001705285,0.0002522983,0.00158224],"category_scores_gemma":[0.001165139,0.0001175521,0.0002231086,0.0003511307,0.0001104048,0.0001821367,0.0003158386,0.0001645152,0.0002899252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009120358,"about_ca_system_score_gemma":0.0001679921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009067091,"about_ca_topic_score_gemma":0.003279469,"domain_scores_codex":[0.9997777,0.00008361034,0.00001986391,0.00003306148,0.00006301502,0.00002262843],"domain_scores_gemma":[0.9995636,0.0001026413,0.0001105235,0.00003290287,0.0001378087,0.00005240628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001285557,0.001331961,0.7845299,0.000523577,0.0001729953,0.0003327162,0.001500777,0.0004190744,0.04111397,0.00004702053,0.001140695,0.1676017],"study_design_scores_gemma":[0.00005754541,0.004043409,0.9858312,0.00005676543,0.00009450206,0.0006946113,0.0008922138,0.00119501,0.006087324,0.00005638672,0.0009729887,0.00001803933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966492,0.0001626771,0.001904768,0.00003727065,0.000007673409,0.0001355604,0.0003372881,0.00004951635,0.000715971],"genre_scores_gemma":[0.994791,0.0002018614,0.00384287,0.00002973035,0.00001200839,0.0002014529,0.0002408516,0.00000546332,0.0006747133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00158224,"threshold_uncertainty_score":0.005293131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0409530298601436,"score_gpt":0.288082820052693,"score_spread":0.2471297901925494,"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."}}