{"id":"W4390666478","doi":"10.1080/15622975.2023.2300795","title":"Multimodal imaging measures in the prediction of clinical response to deep brain stimulation for refractory depression: A machine learning approach","year":2024,"lang":"en","type":"article","venue":"The World Journal of Biological Psychiatry","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates - Health Solutions","keywords":"Neuroimaging; Deep brain stimulation; Artificial intelligence; Cerebral blood flow; Magnetic resonance imaging; Medicine; Nuclear medicine; Computer science; Psychology; Radiology; Neuroscience; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003274926,0.0001224672,0.0003350467,0.0001549505,0.00008306246,0.00001704412,0.0001656895,0.00007302756,0.00001793859],"category_scores_gemma":[0.001284883,0.0000490274,0.000290128,0.0003005129,0.00008600004,0.00004467691,0.00002939754,0.0007143631,0.000001362352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001757562,"about_ca_system_score_gemma":0.00005113393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003563199,"about_ca_topic_score_gemma":0.00000723893,"domain_scores_codex":[0.997716,0.0009561002,0.0007340934,0.0001974892,0.0002418538,0.0001544618],"domain_scores_gemma":[0.9975461,0.001974569,0.0002085468,0.0001419134,0.00005682448,0.00007201357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03179663,0.001567771,0.8868468,0.00005182643,0.0001513511,0.00003938447,0.0004085398,0.001921871,0.001820293,0.0001417151,0.002061043,0.07319275],"study_design_scores_gemma":[0.003164618,0.00312397,0.965687,0.0002045578,0.0001313663,0.00008509436,0.0002518773,0.01907527,0.000009493493,0.003364363,0.004831832,0.00007057907],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9646857,0.005599402,0.005560945,0.02279033,0.0005124877,0.0005990985,0.000009066061,0.00002230295,0.000220642],"genre_scores_gemma":[0.9950253,0.00006985764,0.002610568,0.001894866,0.0003246818,0.00001401966,0.000006751081,0.000009158683,0.00004479203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07884015,"threshold_uncertainty_score":0.3103592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06540673112051625,"score_gpt":0.3753047328114756,"score_spread":0.3098980016909594,"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."}}