{"id":"W2911976009","doi":"10.1016/j.brs.2018.12.825","title":"Towards a personalized approach to rTMS target selection in depression","year":2019,"lang":"en","type":"article","venue":"Brain stimulation","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Depression (economics); Selection (genetic algorithm); Psychology; Physical medicine and rehabilitation; Neuroscience; Computer science; Medicine; Artificial intelligence; Economics","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":[0.0001188225,0.0001041616,0.0001247041,0.0002337412,0.00003322784,0.00001884789,0.0000444242,0.00005933195,0.00005825686],"category_scores_gemma":[0.00003417961,0.0001058512,0.00003609861,0.0005497577,0.000006157384,0.0001424777,0.00001058053,0.00008842571,0.00001168493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007918707,"about_ca_system_score_gemma":0.000007576398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001858737,"about_ca_topic_score_gemma":0.000003518946,"domain_scores_codex":[0.9993545,0.00002336757,0.0001364745,0.0001680439,0.0001460605,0.0001715284],"domain_scores_gemma":[0.999824,0.00003195816,0.00001643088,0.00006456556,0.00002729568,0.00003567892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00008793318,0.00006994919,0.06404616,0.00008720048,0.00003767416,1.9705e-7,0.001998512,0.8321987,0.05044081,0.0009232436,0.004146232,0.04596336],"study_design_scores_gemma":[0.0007047152,0.00002857349,0.5629763,0.00001916709,0.000002498793,9.102139e-7,0.0000651818,0.4289672,0.001103248,0.0001973567,0.005773562,0.000161286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8625049,0.00008026764,0.1195833,0.00009360818,0.0001024116,0.0005086538,0.000001539274,0.0002640945,0.01686119],"genre_scores_gemma":[0.993901,0.000003141247,0.005677608,0.0001243629,0.00004230644,0.00004892899,0.00002123414,0.00001824365,0.0001631905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4989301,"threshold_uncertainty_score":0.4316486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118959676458171,"score_gpt":0.2362230889935644,"score_spread":0.2243271213477473,"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."}}