{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000827939,0.00009014431,0.00008349957,0.00004624345,0.0007524625,0.00006280172,0.0001988127,0.00002753465,0.0001154534],"category_scores_gemma":[0.0005617933,0.00006848387,0.000146474,0.0001140592,0.0002750604,0.000003023316,0.0003008314,0.00005738314,0.00000150243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002396102,"about_ca_system_score_gemma":0.0002437636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005233274,"about_ca_topic_score_gemma":0.00001245218,"domain_scores_codex":[0.998673,0.00006481812,0.000276281,0.0004674981,0.0003249363,0.0001934758],"domain_scores_gemma":[0.9986899,0.00003985268,0.0002306446,0.0008364153,0.0001029651,0.0001002614],"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":[0.003224531,0.0009367598,0.3940704,0.0004166737,0.0001963793,0.00491252,0.0002459381,0.01051634,0.1010231,0.0007574669,0.4688881,0.01481182],"study_design_scores_gemma":[0.0003997747,0.00006407206,0.2076276,0.00002942193,0.0001460069,0.00004531321,0.0001788821,0.0007066202,0.01461836,0.002748628,0.7731305,0.0003048163],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950184,0.000592228,0.0003755517,0.0001190369,0.002729583,0.001012166,0.00005801253,0.00001234962,0.00008271843],"genre_scores_gemma":[0.9957877,0.000006776386,0.00008940872,0.00005964718,0.0000876519,0.002092244,0.0001932104,0.00001421055,0.001669195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3042424,"threshold_uncertainty_score":0.578741,"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."}}