{"id":"W2020445160","doi":"10.1155/2013/303982","title":"A Machine-Learned Predictor of Colonic Polyps Based on Urinary Metabolomics","year":2013,"lang":"en","type":"article","venue":"BioMed Research International","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Colonoscopy; Colorectal cancer; Medicine; Urine; Classifier (UML); Adenomatous polyps; Artificial intelligence; Bowel preparation; Internal medicine; Gold standard (test); Gastroenterology; Computer science; Machine learning; Cancer","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":[0.0006169645,0.000126402,0.000164273,0.0004073528,0.00008198303,0.00003320682,0.0004810089,0.00010693,0.0005300111],"category_scores_gemma":[0.0005432183,0.000105714,0.000108898,0.0002418824,0.0002426936,0.000005536014,0.0002756026,0.0001822932,0.00005430755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000389165,"about_ca_system_score_gemma":0.0001309017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009974479,"about_ca_topic_score_gemma":0.000005545715,"domain_scores_codex":[0.9983642,0.0001475247,0.0002404149,0.0003385807,0.0005869064,0.0003224182],"domain_scores_gemma":[0.9989706,0.00007569768,0.00008091201,0.0003258194,0.0004397605,0.0001071907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005487558,0.0006995685,0.005337127,0.00002571972,0.0003554871,0.000004981713,0.00002114681,0.00002828811,0.9595852,0.00444142,0.02698603,0.001966265],"study_design_scores_gemma":[0.004209049,0.003925984,0.08756933,0.00004558171,0.00002750918,0.00001613728,0.0001502893,0.01395276,0.3533061,0.002640314,0.5336611,0.0004958994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772803,0.001317839,0.0004371605,0.005567406,0.0009395084,0.000579311,0.0003110551,0.00001734582,0.01355003],"genre_scores_gemma":[0.9946943,0.0005687608,0.001404098,0.0001481369,0.0003325064,0.0001169036,0.0002160143,0.00001906246,0.002500236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6062791,"threshold_uncertainty_score":0.5803249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03817795438439802,"score_gpt":0.3451175406894116,"score_spread":0.3069395863050136,"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."}}