{"id":"W2889488196","doi":"10.3390/s19153309","title":"Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"St Joseph's Health Care; Western University","funders":"Ministero dello Sviluppo Economico; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Linear discriminant analysis; Elbow; Support vector machine; Electromyography; Wearable computer; Random forest; Feature selection; Physical medicine and rehabilitation; Artificial intelligence; Feature (linguistics); Computer science; Pattern recognition (psychology); Feature extraction; Receiver operating characteristic; Muscle fatigue; Medicine; Machine learning; Surgery","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006322136,0.00008726562,0.0001395947,0.00008264869,0.00004476955,0.000003984281,0.00004751767,0.00002625999,0.000006348865],"category_scores_gemma":[0.000004205348,0.00008721914,0.00004177155,0.00009052678,0.000006920573,0.0000387988,0.000003366365,0.00003604476,0.00000128136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003949485,"about_ca_system_score_gemma":0.00002394521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003343409,"about_ca_topic_score_gemma":0.00003080282,"domain_scores_codex":[0.9994171,0.000006827473,0.0001843192,0.0001043949,0.0001004533,0.0001868845],"domain_scores_gemma":[0.9997482,0.0000155696,0.00003608578,0.000110979,0.0000436785,0.00004551906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005397187,0.0002627155,0.001795401,0.0005980229,0.0001121553,6.091527e-8,0.007226638,0.4890105,0.004237043,0.0001411138,0.0007613796,0.495801],"study_design_scores_gemma":[0.001049349,0.0001166397,0.09329655,0.00002482344,0.000003460611,2.613562e-8,0.0002104022,0.8931681,0.009181447,0.00003027474,0.002737614,0.0001813184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958635,0.0000132905,0.003326344,0.0000301964,0.00007237316,0.0002931179,0.0000123607,0.00008804479,0.0003008025],"genre_scores_gemma":[0.9820673,0.000002083747,0.01775908,0.00007112278,0.000006941375,0.00001642582,0.00003015404,0.00001912628,0.00002779607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4956197,"threshold_uncertainty_score":0.3556693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01861864418389908,"score_gpt":0.2377687089686148,"score_spread":0.2191500647847157,"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."}}