{"id":"W2620203032","doi":"10.1186/s12984-017-0260-z","title":"Inter-rater reliability of kinesthetic measurements with the KINARM robotic exoskeleton","year":2017,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Hotchkiss Brain Institute; Foothills Medical Centre; University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Innovates; Alberta Innovates - Health Solutions; Heart and Stroke Foundation of Canada","keywords":"Kinesthetic learning; Task (project management); Physical medicine and rehabilitation; Proprioception; Stroke (engine); Activities of daily living; Reliability (semiconductor); Session (web analytics); Psychology; Exoskeleton; Medicine; Physical therapy; Computer science; Developmental psychology","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":[0.01711332,0.000546214,0.0007761735,0.0007709387,0.0004854454,0.0008149085,0.0006318074,0.0005543272,0.0009197596],"category_scores_gemma":[0.03326899,0.0002490738,0.0006404842,0.0005070079,0.0009198968,0.0005087095,0.001088027,0.0004705864,0.0006026605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002875383,"about_ca_system_score_gemma":0.0002613527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007258578,"about_ca_topic_score_gemma":0.001177324,"domain_scores_codex":[0.9817456,0.007466607,0.002541189,0.00328932,0.004496366,0.000460937],"domain_scores_gemma":[0.9508812,0.02519062,0.005717206,0.006072669,0.0115622,0.0005760533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007482239,0.0006808174,0.7994249,0.0008523536,0.00338398,0.0003288834,0.009122547,0.004263006,0.05703566,0.0007226359,0.002123984,0.1145789],"study_design_scores_gemma":[0.0001046598,0.001351568,0.9754555,0.00007276833,0.0003689758,0.0004935109,0.0007899979,0.009828622,0.008894998,0.0004273414,0.002135156,0.00007688969],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9660758,0.0009710288,0.02845459,0.00004921214,0.0001907441,0.0004955093,0.0005522008,0.0001359689,0.003074977],"genre_scores_gemma":[0.99287,0.0001119595,0.005641314,0.00002618538,0.00004221786,0.0003102986,0.0004223101,0.0000530746,0.0005225825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01711332,"threshold_uncertainty_score":0.09050494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563857777250057,"score_gpt":0.2591407853289937,"score_spread":0.2435022075564931,"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."}}