{"id":"W3198518576","doi":"10.1016/j.ebiom.2021.103563","title":"A holistic approach to predicting diabetes risk via biomarkers","year":2021,"lang":"en","type":"letter","venue":"EBioMedicine","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Centre Hospitalier Universitaire de Sherbrooke","funders":"","keywords":"Mahalanobis distance; Context (archaeology); Operationalization; Psychology; Medicine; Bioinformatics; Epistemology; Biology; Philosophy; Artificial intelligence; Computer science","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001017796,0.0007159509,0.001364848,0.001151443,0.0001677574,0.00006687601,0.0003680418,0.0009457167,0.0002342903],"category_scores_gemma":[0.0006794133,0.000621597,0.00030557,0.001283512,0.0002683095,0.00005978001,0.0001892515,0.002581258,0.0000812548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004610968,"about_ca_system_score_gemma":0.0002089867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001151343,"about_ca_topic_score_gemma":0.000001056563,"domain_scores_codex":[0.9951223,0.0002781687,0.0008666843,0.001171443,0.001666682,0.0008947125],"domain_scores_gemma":[0.997213,0.0002168161,0.0003957087,0.001291172,0.000474341,0.0004089799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003811392,0.0001644836,0.005928301,0.001216922,0.0008067584,0.0004723731,0.0001184767,3.506736e-7,0.01399714,0.000001627623,0.9745012,0.002754269],"study_design_scores_gemma":[0.002513148,0.001335779,0.005119387,0.004813751,0.003005511,0.0003731185,0.00008818987,0.001116737,0.00379364,0.0001056457,0.9766842,0.001050886],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.01021675,0.003591032,0.03900405,0.9073647,0.002771348,0.004945361,0.0003754734,0.002632895,0.02909842],"genre_scores_gemma":[0.0340904,0.00008542355,0.03582945,0.8996151,0.01954579,0.0005930489,0.007372478,0.0003704678,0.00249781],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02660061,"threshold_uncertainty_score":0.9997198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03983297852434442,"score_gpt":0.2776391257056004,"score_spread":0.237806147181256,"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."}}