{"id":"W3111933166","doi":"10.37349/emed.2020.00026","title":"Using machine intelligence to uncover Alzheimer’s disease progression heterogeneity","year":2020,"lang":"en","type":"article","venue":"Exploration of Medicine","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Transcriptome; Alzheimer's disease; Function (biology); Class (philosophy); Biology; Computational biology; Disease; Bioinformatics; Neuroscience; Computer science; Medicine; Genetics; Artificial intelligence; Pathology; Gene","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":[0.0001495761,0.000177239,0.0003336291,0.00011138,0.00006569528,0.000006432429,0.00009376308,0.00003614931,0.0002804718],"category_scores_gemma":[0.0003446288,0.0001259968,0.0000709481,0.000418971,0.0001367655,0.0002515855,0.00007227672,0.0001072357,0.00008677549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003907721,"about_ca_system_score_gemma":0.0001781401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003179641,"about_ca_topic_score_gemma":0.000003467077,"domain_scores_codex":[0.9982373,0.00007591519,0.0003658213,0.0003246379,0.0007804068,0.0002158783],"domain_scores_gemma":[0.9981479,0.00003494732,0.0001064087,0.0002717144,0.0002263426,0.001212692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.06356121,0.008239849,0.4169025,0.002616685,0.02368354,0.004330052,0.01341462,0.006020683,0.05381359,0.005813017,0.01650264,0.3851016],"study_design_scores_gemma":[0.02480703,0.03252184,0.0539255,0.007871314,0.0141541,0.00009180513,0.003529337,0.1333908,0.706628,0.005695033,0.01497226,0.002412989],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.558882,0.06778635,0.1838168,0.1793738,0.0006172853,0.007601873,0.000234691,0.0004609487,0.001226242],"genre_scores_gemma":[0.9950088,0.0001809461,0.00256753,0.001691681,0.0003109087,0.00003790235,0.0001742151,0.0000243486,0.000003690059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6528144,"threshold_uncertainty_score":0.5137999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2283086718592086,"score_gpt":0.4358607818124628,"score_spread":0.2075521099532542,"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."}}