{"id":"W3043792204","doi":"10.1186/s12984-020-00727-w","title":"Robotics-assisted visual-motor training influences arm position sense in three-dimensional space","year":2020,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université de Montréal; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Task (project management); Session (web analytics); Proprioception; Motor learning; Artificial intelligence; Robotics; Physical medicine and rehabilitation; Computer science; Generalization; Position (finance); Haptic technology; Psychology; Computer vision; Robot; Medicine; Mathematics; Engineering; Neuroscience","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":[0.0002189399,0.0002627549,0.0001649078,0.0001293753,0.00007196258,0.000184857,0.0001316745,0.0002490615,0.001402001],"category_scores_gemma":[0.001000485,0.00009647392,0.0001699521,0.00005924949,0.0002137377,0.0001628249,0.0002737752,0.0001430119,0.0001249467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006490117,"about_ca_system_score_gemma":0.000118494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003894902,"about_ca_topic_score_gemma":0.0003387375,"domain_scores_codex":[0.9998747,0.00003645943,0.000008905735,0.00002879463,0.0000243122,0.00002679818],"domain_scores_gemma":[0.9997118,0.0001530397,0.00005189632,0.00001685074,0.00003340372,0.00003308408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002384042,0.001165094,0.006823085,0.0002951076,0.00004767116,0.0001228132,0.0002898535,0.001890786,0.9424339,0.00007581598,0.0001066314,0.04436528],"study_design_scores_gemma":[0.0003908951,0.03229275,0.6342832,0.00009273954,0.000312019,0.000950113,0.0007160048,0.02167535,0.3066302,0.0005013797,0.002101918,0.00005344027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976819,0.00009404029,0.001808079,0.00001356066,0.000005045723,0.00001592463,0.00001397251,0.00001983316,0.0003478082],"genre_scores_gemma":[0.9985334,0.00005747567,0.001185439,0.000008916403,0.000002367185,0.00001822299,0.00001242012,0.000002784591,0.0001790332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001402001,"threshold_uncertainty_score":0.004690111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766879896685634,"score_gpt":0.2655616253661711,"score_spread":0.2478928263993147,"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."}}