{"id":"W4283819629","doi":"10.1038/s41598-022-15609-5","title":"Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Inflammatory Bowel Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":239,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; Central South University","keywords":"Lasso (programming language); Logistic regression; Crohn's disease; Regression; Disease; Medicine; Computer science; Statistics; Pathology; Internal medicine; Mathematics; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02124985,0.001221894,0.001824943,0.001518911,0.0005695688,0.001928469,0.001120836,0.001187951,0.002878767],"category_scores_gemma":[0.0381492,0.0005141454,0.001721164,0.001230725,0.000693016,0.0007977734,0.001570957,0.00279119,0.0006032541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004976718,"about_ca_system_score_gemma":0.001455525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001277216,"about_ca_topic_score_gemma":0.001319404,"domain_scores_codex":[0.9887478,0.008480229,0.0006923764,0.001080241,0.0006608173,0.0003385104],"domain_scores_gemma":[0.972105,0.02324801,0.002761664,0.0005683026,0.0009457003,0.0003713311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.003737979,0.0006300004,0.3333154,0.002165334,0.002443099,0.003985493,0.0008884218,0.3370332,0.005124229,0.00952326,0.02259224,0.2785615],"study_design_scores_gemma":[0.00008522936,0.0002735373,0.01274156,0.0001760594,0.0001804829,0.0005403908,0.0001599563,0.9737846,0.0008608082,0.006582932,0.004555526,0.00005889635],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2244864,0.00862774,0.751244,0.005746621,0.0007650996,0.0005300337,0.00267294,0.001341482,0.004585619],"genre_scores_gemma":[0.8147025,0.001670809,0.1768962,0.0007930557,0.0006205176,0.0007962102,0.002339243,0.0002723162,0.001909219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02124985,"threshold_uncertainty_score":0.1123813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0233710799107701,"score_gpt":0.2826826512734105,"score_spread":0.2593115713626404,"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."}}