{"id":"W3134252658","doi":"10.21203/rs.3.rs-201708/v1","title":"Optimal ATN biomarkers and their role in predicting cognitive progress of mild cognitive impairment","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Chongqing Medical University; Eisai; Northern California Institute for Research and Education; Novartis Pharmaceuticals Corporation; BioClinica; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; F. Hoffmann-La Roche; University of Southern California; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Cerebrospinal fluid; Internal medicine; Medicine; Cognitive impairment; Magnetic resonance imaging; Receiver operating characteristic; Biomarker; Area under the curve; Oncology; Neurodegeneration; Gastroenterology; Pathology; Disease; Radiology; Chemistry","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":[0.00107485,0.0005900273,0.000467202,0.00129103,0.0002649942,0.0009924978,0.0003177689,0.0005295824,0.0008216861],"category_scores_gemma":[0.00367065,0.0001550186,0.0002509182,0.0005927744,0.0002718499,0.0005309137,0.0003960719,0.0003970365,0.0002314839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002730786,"about_ca_system_score_gemma":0.000289165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00156387,"about_ca_topic_score_gemma":0.001731799,"domain_scores_codex":[0.9996955,0.0001051086,0.00004288088,0.0000579086,0.00006314031,0.00003544409],"domain_scores_gemma":[0.998006,0.0006734763,0.0006473865,0.0001059551,0.0003051007,0.0002620373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001335652,0.000084655,0.9826617,0.00003658627,0.0001049219,0.000105305,0.0000411831,0.0007076685,0.001341846,0.00005705385,0.0001930676,0.01333036],"study_design_scores_gemma":[0.00002434473,0.0004317379,0.9919282,0.00003820283,0.0001223797,0.0005454319,0.0001252815,0.004250371,0.001352708,0.0006741423,0.0004881912,0.00001888259],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967543,0.001723222,0.0004496105,0.00006575288,0.00001329752,0.000007076574,0.0003545755,0.00001299385,0.0006190392],"genre_scores_gemma":[0.9988695,0.0002068257,0.0005975575,0.00001357494,0.00002116009,0.000005440828,0.000194362,0.00000213354,0.00008926183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00156387,"threshold_uncertainty_score":0.005684435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011508917855659,"score_gpt":0.3920508136982582,"score_spread":0.3619357245197016,"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."}}