{"id":"W3033794115","doi":"10.1016/j.neuroimage.2020.116938","title":"Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression","year":2020,"lang":"en","type":"article","venue":"NeuroImage","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Janssen Alzheimer Immunotherapy Research And Development; Johnson and Johnson Pharmaceutical Research and Development; National Institute on Aging; Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Mauritius Research Council; Wellcome Trust; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Medical Research Council; Meso Scale Diagnostics; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Genentech; University of Warwick; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Fujirebio Europe; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Quantile regression; Neuroimaging; Quantile; Regression; Psychology; Regression analysis; Artificial intelligence; Machine learning; Neuroscience; Computer science; Econometrics; Mathematics","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":[0.009185866,0.0007872795,0.001110494,0.0007767899,0.0002327596,0.00109541,0.001557524,0.001270673,0.001171602],"category_scores_gemma":[0.0254887,0.0003812463,0.0007153003,0.0008067018,0.001403578,0.001446178,0.001450296,0.00152445,0.0002725722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006945198,"about_ca_system_score_gemma":0.000679217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003627185,"about_ca_topic_score_gemma":0.002204158,"domain_scores_codex":[0.9982045,0.0009851444,0.0000706999,0.000417291,0.0002055485,0.0001168676],"domain_scores_gemma":[0.9835445,0.0135843,0.001110728,0.0009919889,0.0005988295,0.0001697332],"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":[0.000259557,0.00008319881,0.01655991,0.0001127198,0.0001720174,0.0001778699,0.0001697277,0.9076678,0.002569831,0.02197661,0.00113363,0.04911716],"study_design_scores_gemma":[0.000007669939,0.00002849831,0.002510015,0.00001062333,0.0000131528,0.0000367417,0.00001083818,0.9831175,0.0005833026,0.0134277,0.0002410383,0.00001288948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07665084,0.0005248878,0.9212903,0.0004624193,0.00002838026,0.00003863958,0.0002573412,0.000302894,0.0004443995],"genre_scores_gemma":[0.9271897,0.0005818514,0.06975823,0.0001933428,0.0001109064,0.0000970449,0.0006747629,0.0001187548,0.001275407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009185866,"threshold_uncertainty_score":0.04858011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.27476170053273,"score_gpt":0.4335543604001308,"score_spread":0.1587926598674009,"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."}}