{"id":"W4323045437","doi":"10.1093/braincomms/fcad048","title":"Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning","year":2023,"lang":"en","type":"article","venue":"Brain Communications","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Occupational Cancer Research Centre; University of Toronto","funders":"National Institute on Aging; NIHR Cambridge Biomedical Research Centre; Medical Research Council; Evelyn Trust; University of California, San Francisco; National Institutes of Health; Wolfson Foundation; University College London; National Institute of Mental Health; UK Dementia Research Institute; National Institute for Health and Care Research; Brain Research UK; Wellcome Trust; Alzheimer's Society; Engineering and Physical Sciences Research Council; UK Research and Innovation","keywords":"Progressive supranuclear palsy; Corticobasal degeneration; Atrophy; Parkinsonism; Psychology; Pathology; Medicine; Disease","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002003208,0.0001213316,0.0002466083,0.0002393686,0.0001415415,0.00001353218,0.0002209195,0.0000533651,0.00006770036],"category_scores_gemma":[0.0001436217,0.0001195678,0.00009075761,0.0004902532,0.00004704723,0.00007671743,0.0002480779,0.0002032204,0.00002268104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007744662,"about_ca_system_score_gemma":0.00008119888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009401533,"about_ca_topic_score_gemma":0.0001651534,"domain_scores_codex":[0.999,0.0001788717,0.0002828246,0.0001700885,0.0001764961,0.0001917328],"domain_scores_gemma":[0.9988053,0.0001636441,0.0001219498,0.0007674918,0.0000599893,0.0000816585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001504223,0.0007604063,0.9724577,0.0002282015,0.0002065127,0.0001240549,0.001829282,0.001292396,0.009876758,0.0006032124,0.00003489843,0.01243616],"study_design_scores_gemma":[0.006387862,0.0004130748,0.8380023,0.001215712,0.0002009065,0.00003012261,0.001281566,0.1381202,0.001649919,0.0007322985,0.01159646,0.0003695553],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970707,0.0006240513,0.0001054379,0.001410376,0.00002465633,0.0003861308,0.000066759,0.00009610804,0.0002157458],"genre_scores_gemma":[0.9958234,0.0002724261,0.00322094,0.00009330533,0.00001427173,0.00003827862,0.0004191008,0.00003333824,0.00008489958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1368278,"threshold_uncertainty_score":0.4875834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05465336840566661,"score_gpt":0.3281132840164713,"score_spread":0.2734599156108047,"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."}}