{"id":"W4285210911","doi":"10.1002/trc2.12303","title":"Brain simulation augments machine‐learning–based classification of dementia","year":2022,"lang":"en","type":"article","venue":"Alzheimer s & Dementia Translational Research & Clinical Interventions","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Neuroimaging; Dementia; Computer science; Positron emission tomography; Artificial intelligence; Magnetic resonance imaging; Machine learning; Functional magnetic resonance imaging; Neuroscience; Pattern recognition (psychology); Psychology; Medicine; Disease; Pathology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007953039,0.0005635886,0.0004508675,0.00067696,0.0001823922,0.0006815831,0.0005658547,0.0005754055,0.001444091],"category_scores_gemma":[0.005087712,0.0002565058,0.0005576789,0.0002729909,0.0003880365,0.0005759954,0.000671649,0.0004331572,0.0002481246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005147588,"about_ca_system_score_gemma":0.0005197884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002983213,"about_ca_topic_score_gemma":0.003019511,"domain_scores_codex":[0.9998261,0.00008852849,0.000009231119,0.00004079951,0.00002335784,0.00001202618],"domain_scores_gemma":[0.9990433,0.0006562975,0.00009487045,0.00009506698,0.00006385764,0.00004659453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003408292,0.0001862036,0.01713236,0.000134302,0.0001976296,0.0001331687,0.0001327874,0.9077263,0.009481641,0.001787353,0.0006395568,0.06210783],"study_design_scores_gemma":[0.000007905454,0.00003542067,0.001788365,0.000006762403,0.00001112196,0.00003710143,0.000007020637,0.9944676,0.001230243,0.002242186,0.0001607516,0.000005517976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.660367,0.0004094301,0.3333036,0.0007496576,0.00007717923,0.0001111737,0.0004323957,0.001334034,0.003215665],"genre_scores_gemma":[0.9749321,0.00008505274,0.02443846,0.00004533162,0.000016137,0.00004035002,0.0001814595,0.00003868673,0.000222394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002983213,"threshold_uncertainty_score":0.005931675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.504852920493066,"score_gpt":0.5198613161932234,"score_spread":0.01500839570015744,"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."}}