{"id":"W4379768085","doi":"10.1002/alz.13164","title":"Profiling and predicting distinct tau progression patterns: An unsupervised data‐driven approach to flortaucipir positron emission tomography","year":2023,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Siemens Medical Solutions USA; University of Southern California; Biogen; Northern California Institute for Research and Education; Massachusetts General Hospital; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; National Institute on Aging; Alzheimer's Association","keywords":"Positron emission tomography; Neuroimaging; Alzheimer's Disease Neuroimaging Initiative; Dementia; Internal medicine; Medicine; Alzheimer's disease; Psychology; Nuclear medicine; Disease; Neuroscience","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":[0.003843219,0.001005831,0.001407507,0.002297101,0.0005925978,0.001541929,0.001295216,0.001088269,0.000569204],"category_scores_gemma":[0.007972106,0.0004523042,0.001916553,0.00131203,0.0007400256,0.0006198454,0.0009384857,0.00129663,0.0003653804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001157582,"about_ca_system_score_gemma":0.001723877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008080828,"about_ca_topic_score_gemma":0.01185848,"domain_scores_codex":[0.9985119,0.0006115091,0.0001000489,0.0004494993,0.0002044784,0.000122545],"domain_scores_gemma":[0.996691,0.00195273,0.0003831967,0.0003221137,0.0005198083,0.0001311195],"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":[0.001455284,0.001511018,0.16947,0.0004799732,0.001725907,0.0004720419,0.0004698905,0.2675299,0.02101559,0.002744981,0.006233413,0.5268919],"study_design_scores_gemma":[0.00005151893,0.0002016334,0.02524923,0.00004213556,0.000104713,0.0001994271,0.0000843239,0.962284,0.004001422,0.006378838,0.001329775,0.00007290168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3099075,0.001095201,0.6798923,0.001178164,0.00006447214,0.0008827433,0.003512061,0.002344706,0.001122868],"genre_scores_gemma":[0.7087938,0.0002693978,0.2826755,0.0003562508,0.00009338462,0.0007726099,0.005803141,0.0001909692,0.001044936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008080828,"threshold_uncertainty_score":0.02032512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05586688100968361,"score_gpt":0.3486212041139914,"score_spread":0.2927543231043078,"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."}}