{"id":"W4412163708","doi":"10.1158/1557-3265.aimachine-a043","title":"Abstract A043: Mutational profiling and machine learning for risk stratification and biomarker identification in intraductal papillary mucinous neoplasms progressing to pancreatic cancer","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Pancreatic and Hepatic Oncology Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Risk stratification; Biomarker; Medicine; Pancreatic cancer; Pathology; Profiling (computer programming); Pancreas; Oncology; Internal medicine; Cancer research; Cancer; Biology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00144345,0.0004645694,0.0004148251,0.001388862,0.0003034061,0.000842304,0.000311709,0.0003950064,0.001036838],"category_scores_gemma":[0.00306025,0.0001726701,0.0006143329,0.0006864017,0.0002273813,0.0003161043,0.0004030032,0.0005486471,0.0004476627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003350929,"about_ca_system_score_gemma":0.0005781168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001909689,"about_ca_topic_score_gemma":0.002127006,"domain_scores_codex":[0.9993992,0.0002623803,0.00005273046,0.0001533618,0.00009175279,0.00004055914],"domain_scores_gemma":[0.9990178,0.0004976687,0.0001709867,0.00009102928,0.0001594484,0.00006296605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002059502,0.0005247516,0.6738623,0.0001875586,0.0004531969,0.0003743088,0.0001521121,0.05474041,0.08598351,0.0008171134,0.00218064,0.1786646],"study_design_scores_gemma":[0.00005422147,0.0007537645,0.2773553,0.00003593109,0.0001833275,0.0006253019,0.0001158406,0.6856435,0.03148112,0.001864459,0.001846053,0.00004116236],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9463602,0.0006644396,0.04949521,0.0002516976,0.0000241754,0.00007405043,0.001855221,0.0004289233,0.0008461459],"genre_scores_gemma":[0.9757615,0.0001215191,0.02199811,0.00004786886,0.00001365641,0.00005132583,0.001569719,0.00002414382,0.0004122811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001909689,"threshold_uncertainty_score":0.007633746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1859227310466425,"score_gpt":0.5364256847718474,"score_spread":0.3505029537252049,"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."}}