{"id":"W2899192547","doi":"10.3389/fphar.2018.01188","title":"Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression","year":2018,"lang":"en","type":"article","venue":"Frontiers in Pharmacology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Toronto; Centre for Addiction and Mental Health; University Health Network; University of British Columbia","funders":"Canadian Institutes of Health Research; H. Lundbeck A/S; Vancouver Coastal Health Research Institute; Canadian Network for Mood and Anxiety Treatments; Pfizer; Fondation Brain Canada; Michael Smith Health Research BC; Bristol-Myers Squibb","keywords":"Electroencephalography; Transcranial magnetic stimulation; Receiver operating characteristic; Audiology; Medicine; Psychology; Neuroscience; Stimulation; Internal medicine","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":[0.001182835,0.0003299834,0.0002531221,0.0006934207,0.00006481297,0.0002870968,0.000149047,0.000248901,0.0005807484],"category_scores_gemma":[0.004087769,0.00006972813,0.0002505357,0.0003184999,0.0001320719,0.0002209446,0.0001720922,0.0002541406,0.0001235325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001421152,"about_ca_system_score_gemma":0.00009733903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003382779,"about_ca_topic_score_gemma":0.0005645695,"domain_scores_codex":[0.9997415,0.0001267792,0.00002780392,0.00003109069,0.00005730621,0.00001555063],"domain_scores_gemma":[0.9982703,0.001180706,0.000283648,0.00006794924,0.000137582,0.00005973633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003615248,0.0003994548,0.7689862,0.0002705409,0.0006230938,0.0002748655,0.0002348141,0.01061872,0.0354762,0.0001639038,0.0006660775,0.1786708],"study_design_scores_gemma":[0.00004017,0.000802916,0.9507665,0.00002044208,0.00009646102,0.0004818093,0.00006713532,0.04277361,0.004398415,0.0003046418,0.000230828,0.00001701547],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908962,0.0005255266,0.007698369,0.00005585843,0.00001039166,0.0000395872,0.0002128998,0.00003991809,0.0005212771],"genre_scores_gemma":[0.9970293,0.0001169686,0.00255381,0.00001227666,0.00001175165,0.00002050917,0.0001609526,0.000003631273,0.00009063158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001182835,"threshold_uncertainty_score":0.006255507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0143122404206116,"score_gpt":0.3157761507846094,"score_spread":0.3014639103639978,"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."}}