{"id":"W4210572355","doi":"10.1002/advs.202104538","title":"Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment","year":2022,"lang":"en","type":"article","venue":"Advanced Science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Eisai; Servier; Beijing Normal University; Genentech; IXICO; National Natural Science Foundation of China; Pfizer; Novartis Pharmaceuticals Corporation; F. Hoffmann-La Roche; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Abnormality; Dementia; Neuroimaging; Cognitive impairment; Cognition; Medicine; Disease; Internal medicine; Bioinformatics; Psychology; Neuroscience; Biology; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0005000188,0.0002334662,0.0003022305,0.001802596,0.0002156895,0.0004124606,0.0002468148,0.0003033477,0.0009111415],"category_scores_gemma":[0.002606751,0.0001139068,0.0003597315,0.0009115817,0.0003271137,0.0004194176,0.0004961591,0.0001607094,0.0001901504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003159019,"about_ca_system_score_gemma":0.0001714361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003502893,"about_ca_topic_score_gemma":0.004084445,"domain_scores_codex":[0.9996672,0.0001132256,0.00002775631,0.0001036498,0.00004219138,0.00004605277],"domain_scores_gemma":[0.9990737,0.0002726843,0.0003323478,0.0000979631,0.0001500959,0.00007310026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001642405,0.0001113365,0.8871036,0.0001475799,0.0005935298,0.0004620544,0.0008460789,0.01354334,0.02449973,0.0021064,0.001295679,0.06764833],"study_design_scores_gemma":[0.00001618665,0.0001789316,0.9603608,0.00001716186,0.0001419107,0.0006266502,0.0004041141,0.03246035,0.001363644,0.003698522,0.0007080549,0.00002373426],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995092,0.0002237416,0.003605921,0.00004983425,0.000003313836,0.00001378653,0.0003540096,0.00002692967,0.0006305314],"genre_scores_gemma":[0.9984158,0.00005540584,0.0009428496,0.000008298809,0.000005651271,0.00001027441,0.0004227259,0.000003470932,0.0001355852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003502893,"threshold_uncertainty_score":0.006965041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358018304088768,"score_gpt":0.3013514475775353,"score_spread":0.2877712645366476,"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."}}