{"id":"W4403880952","doi":"10.3389/frobt.2024.1462558","title":"Editorial: Human-centered solutions and synergies across robotic and digital systems for rehabilitation","year":2024,"lang":"en","type":"editorial","venue":"Frontiers in Robotics and AI","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Computer science; Human–computer interaction; Rehabilitation; Human–robot interaction; Artificial intelligence; Robot; Data science; Neuroscience; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003821033,0.000301605,0.0007631436,0.0002667043,0.0001803802,0.0004537429,0.00005464013,0.0007128262,4.135323e-7],"category_scores_gemma":[0.001344976,0.000267953,0.0001246146,0.0001151651,0.0003573069,0.0001875651,0.00009520657,0.0005286286,6.970103e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001693183,"about_ca_system_score_gemma":0.0001295692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003085939,"about_ca_topic_score_gemma":0.000009364875,"domain_scores_codex":[0.998196,0.00003767628,0.0005134327,0.0005540358,0.0003445236,0.0003543043],"domain_scores_gemma":[0.9984196,0.0008803284,0.0001126313,0.0001920327,0.0002524599,0.0001429633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009423013,0.00006540771,0.001859556,0.003464903,0.0001338644,0.000002483501,0.0005030866,0.0005764445,0.000005645689,0.0002452477,0.9926552,0.0003939815],"study_design_scores_gemma":[0.002772482,0.001065231,0.0006299748,0.002639167,0.0003355483,0.000006518986,0.003271053,0.007643774,2.371895e-7,0.002096752,0.9791222,0.000417035],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0007801678,0.01505141,0.004502505,0.0007920438,0.9773117,0.00100477,0.0004711022,0.00005570784,0.00003058425],"genre_scores_gemma":[0.00686687,0.002761299,0.003411064,0.00001621194,0.9837221,0.0001443813,0.0007116584,0.00009412153,0.002272299],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.01353292,"threshold_uncertainty_score":0.9999773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151884728289418,"score_gpt":0.2951929642898519,"score_spread":0.2836741170069578,"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."}}