{"id":"W2023983729","doi":"10.4103/0028-3886.82714","title":"Non-normalized individual analysis of statistical parametric mapping for clinical fMRI","year":2011,"lang":"en","type":"article","venue":"Neurology India","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistical parametric mapping; Spatial normalization; Functional magnetic resonance imaging; Medicine; Normalization (sociology); Brain mapping; Statistical analysis; Magnetic resonance imaging; Parametric statistics; Artificial intelligence; Functional imaging; Pattern recognition (psychology); Neuroscience; Radiology; Computer science; Psychology; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01843545,0.001613501,0.002195556,0.003651439,0.000987384,0.001681901,0.002244314,0.000708938,0.01770366],"category_scores_gemma":[0.0730162,0.0008015801,0.001406612,0.003011954,0.002505897,0.001682161,0.001860501,0.002036504,0.001769632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009278136,"about_ca_system_score_gemma":0.002704942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001083472,"about_ca_topic_score_gemma":0.001727382,"domain_scores_codex":[0.9880228,0.007361702,0.001093857,0.001422376,0.001861675,0.0002375873],"domain_scores_gemma":[0.9571453,0.0308802,0.002858148,0.006481107,0.002303591,0.0003316999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006524001,0.001073552,0.02186566,0.003552107,0.002697273,0.003257252,0.002348186,0.02457509,0.05184193,0.04142943,0.02804806,0.8127874],"study_design_scores_gemma":[0.0009270634,0.006024103,0.1258016,0.0006526134,0.0008419855,0.007270257,0.001229715,0.633092,0.05508724,0.1083347,0.06011694,0.0006218154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04241997,0.0004137972,0.9475247,0.0002547431,0.0002748413,0.002148886,0.001120994,0.003327593,0.002514473],"genre_scores_gemma":[0.1896994,0.000127744,0.7964007,0.0000727187,0.00007036218,0.01032699,0.0006806848,0.001204938,0.001416495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01843545,"threshold_uncertainty_score":0.09749711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1667562301701907,"score_gpt":0.3601720313221325,"score_spread":0.1934158011519418,"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."}}