{"id":"W2070638823","doi":"10.1016/j.biopsych.2008.06.014","title":"Optimizing the Design and Analysis of Clinical Functional Magnetic Resonance Imaging Research Studies","year":2008,"lang":"en","type":"review","venue":"Biological Psychiatry","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":false,"ca_institutions":"Baycrest Hospital; University of Toronto; Mental Health Research Canada","funders":"Roche; National Institute of Mental Health; James S. McDonnell Foundation","keywords":"Functional magnetic resonance imaging; Magnetic resonance imaging; Nuclear magnetic resonance; Medicine; Physics; Radiology","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":[0.001373193,0.0002160922,0.001693333,0.0002151467,0.0002285519,0.00000607013,0.0001821097,0.0002386634,0.0000288702],"category_scores_gemma":[0.0004642574,0.0001043782,0.0006188946,0.001210684,0.001141319,0.0000139958,0.0001682745,0.0008056937,0.000004374595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002848676,"about_ca_system_score_gemma":0.0001907909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002037002,"about_ca_topic_score_gemma":5.792058e-7,"domain_scores_codex":[0.9975117,0.0005492957,0.0009270617,0.0005506882,0.000224636,0.000236555],"domain_scores_gemma":[0.9967831,0.002184818,0.0002662663,0.0004851193,0.000200772,0.00007990656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004665223,0.0001406742,0.001547159,0.0003032483,0.0002187301,0.000003534132,0.000009117809,0.000004515459,2.430344e-7,0.001677154,0.005993172,0.9900558],"study_design_scores_gemma":[0.0001530854,0.0002952611,0.004537593,0.001461916,0.001494619,0.00005499918,0.00007060955,0.0001516452,6.392918e-8,0.001009197,0.9906347,0.0001362598],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001214539,0.9868723,0.0113707,0.0004946174,0.00009822191,0.0009171282,0.00001889417,0.0000444607,0.0001715059],"genre_scores_gemma":[0.00001497649,0.8886763,0.1105741,0.0001257475,0.0002108989,0.0002362333,0.00003474868,0.00001262896,0.0001144065],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9899195,"threshold_uncertainty_score":0.4256421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5478367019425008,"score_gpt":0.5657028774486977,"score_spread":0.01786617550619696,"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."}}