{"id":"W2424203863","doi":"10.1002/mus.25224","title":"Interleaved neuromuscular electrical stimulation reduces muscle fatigue","year":2016,"lang":"en","type":"article","venue":"Muscle & Nerve","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University Health Network; University of Alberta","funders":"Canadian Institutes of Health Research; University of Alberta; Craig H. Neilsen Foundation","keywords":"Electromyography; Muscle fatigue; Stimulation; Medicine; Motor unit; Physical medicine and rehabilitation; Stimulus (psychology); Electrical muscle stimulation; Nerve stimulator; Rehabilitation; Motor unit recruitment; Trunk; Muscle contraction; Anesthesia; Biomedical engineering; Physical therapy; Anatomy; Internal medicine; Psychology; Biology","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":[0.0001821421,0.0002730955,0.0002680084,0.0001307231,0.00005244271,0.0001063007,0.0002412731,0.0002213311,0.002393517],"category_scores_gemma":[0.0004803762,0.00008370722,0.0001323279,0.0000653821,0.0001272006,0.000253703,0.0002230462,0.000349709,0.0002239796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001005791,"about_ca_system_score_gemma":0.0001068995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002152956,"about_ca_topic_score_gemma":0.0006269024,"domain_scores_codex":[0.9999148,0.0000179845,0.000009915944,0.00001977475,0.00002395928,0.00001366225],"domain_scores_gemma":[0.9998543,0.0000541159,0.0000423958,0.00001365601,0.00001884287,0.00001660022],"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":[0.005924385,0.001930568,0.002562115,0.0005401626,0.00009571142,0.00006362817,0.00006985349,0.001493511,0.8688917,0.0001218641,0.0002179274,0.1180886],"study_design_scores_gemma":[0.0009835319,0.06461219,0.1573237,0.0002593929,0.0003648068,0.001353751,0.0001911158,0.01787014,0.7477128,0.0006512675,0.008634132,0.0000432027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869653,0.001782562,0.009959323,0.00005322231,0.00004264803,0.00005191812,0.00004444809,0.0001001877,0.001000415],"genre_scores_gemma":[0.9897342,0.0005493456,0.00799819,0.0000543197,0.00002799901,0.000061026,0.00008435116,0.00001777995,0.001472782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002393517,"threshold_uncertainty_score":0.008007109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02029592100587918,"score_gpt":0.2329559728589625,"score_spread":0.2126600518530833,"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."}}