{"id":"W2922242284","doi":"10.5194/ms-10-107-2019","title":"Design of a robot-assisted exoskeleton for passive wrist and forearm rehabilitation","year":2019,"lang":"en","type":"article","venue":"Mechanical sciences","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Gaziantep Üniversitesi","keywords":"Exoskeleton; Kinematics; Wrist; Engineering; Forearm; Simulation; Robot; Process (computing); Robot end effector; Computer science; Artificial intelligence; Medicine; Physics; Anatomy","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":[],"consensus_categories":[],"category_scores_codex":[0.000612782,0.00006461114,0.0002130126,0.00007923914,0.00005460885,0.00001180263,0.0000550716,0.00006033934,0.00005411786],"category_scores_gemma":[0.0006435739,0.00004348841,0.00007466633,0.0002070684,0.0001654325,0.00007560364,0.00001692548,0.00004004375,0.000004996049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002704397,"about_ca_system_score_gemma":0.00007803164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002464858,"about_ca_topic_score_gemma":0.0000031207,"domain_scores_codex":[0.9991478,0.00005056127,0.000205673,0.0002416135,0.0002206259,0.0001337092],"domain_scores_gemma":[0.9982415,0.001412939,0.00008126599,0.00009646025,0.0001000199,0.0000678196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00101935,0.0006468278,0.03685234,0.0009207988,0.00008572507,0.000001266544,0.001088036,0.0002982258,0.740198,0.04997237,0.001168707,0.1677484],"study_design_scores_gemma":[0.009303428,0.04683012,0.7467629,0.000905952,0.0002625005,0.0000553414,0.007455972,0.08185517,0.05863607,0.04293594,0.004321839,0.0006747275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9299632,0.0000926506,0.06515385,0.002727698,0.00025648,0.001382693,0.000003290879,0.00002542248,0.0003946586],"genre_scores_gemma":[0.9265531,0.000009195131,0.0730637,0.00009057918,0.00002150801,0.00003803187,0.000001759826,0.000004045959,0.0002181319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7099106,"threshold_uncertainty_score":0.1773406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02967280449872613,"score_gpt":0.3086488639355977,"score_spread":0.2789760594368716,"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."}}