{"id":"W4308099456","doi":"10.1371/journal.pone.0276392","title":"SPARE-Tau: A flortaucipir machine-learning derived early predictor of cognitive decline","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"DoD Alzheimer's Disease Neuroimaging Initiative; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Servier; Eisai; San Antonio Medical Foundation; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Neuroimaging; Medicine; Logistic regression; Biomarker; Spare part; Psychology; Internal medicine; Oncology; 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":[0.001717002,0.0008350876,0.0006888867,0.001598811,0.0002813495,0.0006158706,0.0005731996,0.0005417033,0.001713163],"category_scores_gemma":[0.003107761,0.0001482558,0.0006637002,0.0006848087,0.0002317721,0.000306624,0.0004195157,0.0006061384,0.0004851234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004418702,"about_ca_system_score_gemma":0.000405252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001959408,"about_ca_topic_score_gemma":0.003443153,"domain_scores_codex":[0.9996969,0.00007559129,0.00002848624,0.000104667,0.00005857564,0.00003575225],"domain_scores_gemma":[0.9986699,0.0004191787,0.0004219507,0.0001241936,0.0002284929,0.0001364213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005137594,0.000350452,0.875046,0.0002409135,0.0009906422,0.0003317499,0.00009927301,0.0122676,0.007312013,0.0002836338,0.00308725,0.09485283],"study_design_scores_gemma":[0.0002271672,0.002003829,0.8260944,0.0001343953,0.0009365227,0.002538421,0.0001066061,0.1473439,0.01277815,0.003338474,0.004380965,0.0001170684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807333,0.001820417,0.01056774,0.0001823065,0.00004660964,0.00006710969,0.004677444,0.0004128029,0.001492255],"genre_scores_gemma":[0.9870996,0.0002476787,0.008548346,0.00005227587,0.00004874339,0.00008365397,0.0032613,0.00003078156,0.0006276214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001959408,"threshold_uncertainty_score":0.00908047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04085761259130473,"score_gpt":0.2837404644908129,"score_spread":0.2428828518995081,"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."}}