{"id":"W4389433641","doi":"10.21203/rs.3.rs-3694179/v1","title":"Refining risk prediction in pediatric Acute Lymphoblastic Leukemia through DNA methylation profiling","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Acute Lymphoblastic Leukemia research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"Science for Life Laboratory; Knut och Alice Wallenbergs Stiftelse; Vetenskapsrådet; Swedish Cancer Foundation","keywords":"DNA methylation; Cohort; Oncology; Medicine; CpG site; Epigenetics; Risk stratification; Internal medicine; Methylation; Lymphoblastic Leukemia; Risk assessment; Bioinformatics; Leukemia; Biology; DNA; Computer science; Gene; Gene expression; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001134166,0.0003631667,0.0004026555,0.0008933516,0.0002163635,0.0004466529,0.0002643316,0.0001782312,0.0003489823],"category_scores_gemma":[0.00245211,0.0001186829,0.0002746067,0.000907573,0.0001533062,0.0001276957,0.0003403405,0.0004160816,0.0001208033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005219514,"about_ca_system_score_gemma":0.001095571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0378971,"about_ca_topic_score_gemma":0.06267083,"domain_scores_codex":[0.9996327,0.0001410424,0.0000230733,0.00007307123,0.00008222455,0.00004781693],"domain_scores_gemma":[0.9992995,0.0002572415,0.0002011608,0.0000543077,0.0001466796,0.00004108648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002718036,0.00002871725,0.9634064,0.00003052825,0.00009721214,0.00008135682,0.0001086968,0.007871342,0.007009338,0.00008404782,0.000330042,0.0206805],"study_design_scores_gemma":[0.00002480904,0.0001678464,0.9285448,0.00003624392,0.0001532362,0.0003073224,0.0002550367,0.05875111,0.009513379,0.0004439354,0.00177878,0.00002343673],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942725,0.0005686195,0.003800573,0.00005817337,0.000005678161,0.0000188971,0.0009023349,0.0000472621,0.0003260554],"genre_scores_gemma":[0.9950398,0.0001872114,0.003870574,0.00001597077,0.000005584645,0.000008181238,0.0007336542,0.000005782111,0.0001331624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0378971,"threshold_uncertainty_score":0.07535303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07669261971059224,"score_gpt":0.3947854832442048,"score_spread":0.3180928635336125,"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."}}