{"id":"W4392614927","doi":"10.1038/s41375-024-02210-0","title":"Redefining high risk multiple myeloma with an APOBEC/Inflammation-based classifier","year":2024,"lang":"en","type":"letter","venue":"Leukemia","topic":"Multiple Myeloma Research and Treatments","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; Simon Fraser University; Terry Fox Research Institute; University of British Columbia","funders":"Canadian Institutes of Health Research; National Cancer Institute; BC Cancer Foundation; University of British Columbia; Deutsche Forschungsgemeinschaft; Michael Smith Health Research BC; Leukemia and Lymphoma Society of Canada; Simon Fraser University; Cancer Prevention and Research Institute of Texas","keywords":"APOBEC; Multiple myeloma; Classifier (UML); Inflammation; Medicine; Biology; Internal medicine; Genetics; Computer science; Genome; Gene; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007113476,0.0005020407,0.0007235559,0.0007265838,0.0003439116,0.001238098,0.0005664326,0.002005776,0.007019024],"category_scores_gemma":[0.005787384,0.0001440968,0.0006387326,0.0002839751,0.0002719624,0.0004833436,0.0003292151,0.002818691,0.00243019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014063,"about_ca_system_score_gemma":0.0004266595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002691565,"about_ca_topic_score_gemma":0.005112786,"domain_scores_codex":[0.9996952,0.000103889,0.00001986114,0.0000591717,0.00008175349,0.00004007207],"domain_scores_gemma":[0.9986651,0.0007167592,0.00008897985,0.0000727714,0.0003435477,0.0001128198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001740124,0.0002555434,0.02749121,0.0004210876,0.0002746201,0.002025176,0.00005534335,0.00987249,0.009065421,0.004970638,0.4152239,0.5286044],"study_design_scores_gemma":[0.001275092,0.001284697,0.04362717,0.0009245069,0.001086553,0.01207742,0.0002118429,0.5218926,0.02783457,0.07063119,0.318842,0.0003124178],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1098102,0.02299529,0.1959722,0.5692194,0.02163912,0.0006570906,0.008241813,0.006759059,0.06470588],"genre_scores_gemma":[0.7697551,0.006195735,0.1042175,0.07300816,0.01866247,0.0003924186,0.002327779,0.0003032605,0.02513759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007019024,"threshold_uncertainty_score":0.02348095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02612859339725811,"score_gpt":0.2744422811506523,"score_spread":0.2483136877533942,"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."}}