{"id":"W2909343664","doi":"10.1002/jum.14909","title":"Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case‐Control Study","year":2019,"lang":"en","type":"article","venue":"Journal of Ultrasound in Medicine","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Joseph’s Healthcare Hamilton; McMaster University; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Support vector machine; Medicine; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Radial basis function kernel; Local binary patterns; Hyperparameter; Test set; Correlation; Cross-validation; Correlation coefficient; Machine learning; Histogram; Computer science; Image (mathematics); Kernel method; Mathematics","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":[0.002398374,0.0001596866,0.000680163,0.0003029969,0.00002885234,0.000006141273,0.0001233126,0.00005412905,0.0002970226],"category_scores_gemma":[0.003393919,0.0001184171,0.0001208753,0.0003249792,0.00005646174,0.0001185415,0.000004991025,0.0002814349,4.346998e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009143515,"about_ca_system_score_gemma":0.00003224532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002634025,"about_ca_topic_score_gemma":0.00008134186,"domain_scores_codex":[0.9984135,0.0001306905,0.0008060915,0.0001171115,0.000310567,0.000222013],"domain_scores_gemma":[0.9951029,0.004278259,0.0002155273,0.0001603874,0.000163791,0.00007910995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003095159,0.0005696175,0.9738579,0.0003536415,0.0006549562,0.0003630133,0.002201433,0.0005057495,0.009892184,0.0000323726,0.004576091,0.006683491],"study_design_scores_gemma":[0.007766744,0.004817832,0.9820979,0.0001575,0.0001848142,0.001097689,0.002452647,0.0004277314,0.00005698748,0.000168896,0.0006091839,0.0001620831],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943271,0.0005029233,0.003330411,0.000278729,0.0005860759,0.0007799565,0.00002120789,0.00001490072,0.0001587284],"genre_scores_gemma":[0.9995084,0.00008681769,0.0001093443,0.00008650142,0.0001558406,0.00001494465,0.00000204246,0.00001838946,0.00001776705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009835197,"threshold_uncertainty_score":0.4828908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01592182963120779,"score_gpt":0.2687615578537497,"score_spread":0.2528397282225419,"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."}}