{"id":"W2109945587","doi":"10.1109/bsn.2006.57","title":"Using Wearable Sensors to Measure Motor Abilities following Stroke","year":2006,"lang":"en","type":"article","venue":"","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McMaster University","keywords":"Wearable computer; Rehabilitation; Physical medicine and rehabilitation; Stroke (engine); Computer science; Medicine; Physical therapy; Engineering; Embedded system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000188318,0.000113006,0.0002363821,0.0001593216,0.00007367056,0.0000234928,0.00003249062,0.00007020829,0.0002984476],"category_scores_gemma":[0.0001925399,0.00008930745,0.000250139,0.000139906,0.00002451021,0.0000766569,0.00001355978,0.00008406818,0.00008589585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001358035,"about_ca_system_score_gemma":0.00007761314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005350788,"about_ca_topic_score_gemma":0.00002474959,"domain_scores_codex":[0.9990031,0.00003238455,0.0002196209,0.0002029275,0.0003070946,0.0002348324],"domain_scores_gemma":[0.9994636,0.0001263917,0.00002126137,0.0001937581,0.00008352906,0.0001114795],"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":[0.0002910477,0.0002913288,0.369025,0.0002110136,0.0001714434,0.0000386418,0.000573619,0.00221829,0.6192216,0.000848364,0.005142953,0.001966733],"study_design_scores_gemma":[0.009908967,0.00223382,0.7411814,0.001723088,0.0007117012,0.0002134251,0.02262576,0.01019771,0.1180879,0.0009166137,0.09040254,0.001797043],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9581211,0.000194829,0.001001373,0.0006316882,0.000408903,0.000394185,0.00000506878,0.0001124877,0.03913038],"genre_scores_gemma":[0.9272805,0.000001546122,0.01968352,0.000266595,0.0002328008,0.000008104064,0.000001578044,0.00001882542,0.05250655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5011337,"threshold_uncertainty_score":0.3641852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02836650845857698,"score_gpt":0.2837364241303466,"score_spread":0.2553699156717696,"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."}}