{"id":"W2804219602","doi":"10.1182/blood-2018-03-838136","title":"A gene signature that distinguishes conventional and leukemic nonnodal mantle cell lymphoma helps predict outcome","year":2018,"lang":"en","type":"article","venue":"Blood","topic":"Lymphoma Diagnosis and Treatment","field":"Medicine","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; BC Cancer Agency","funders":"European Regional Development Fund; Instituto de Salud Carlos III; National Cancer Institute; Agència de Gestió d'Ajuts Universitaris i de Recerca; National Institutes of Health; Ministerio de Economía y Competitividad; Generalitat de Catalunya; Centres de Recerca de Catalunya","keywords":"Mantle cell lymphoma; Lymphoma; Outcome (game theory); Gene signature; Signature (topology); Biology; Computational biology; Cancer research; Bioinformatics; Oncology; Gene; Medicine; Genetics; Immunology; Gene expression; Mathematics","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":[0.0000782794,0.0001871043,0.0002639654,0.00005112333,0.0001187173,0.00003194703,0.00006438938,0.0001143318,0.0001738771],"category_scores_gemma":[0.00003573137,0.0001423838,0.00009976433,0.0000674283,0.0001299301,0.0000429562,0.00006649492,0.00004986075,0.00008327138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006977555,"about_ca_system_score_gemma":0.00009335527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005593597,"about_ca_topic_score_gemma":0.000009954644,"domain_scores_codex":[0.9989552,0.00001947767,0.0001924961,0.0003257771,0.0002588127,0.0002482726],"domain_scores_gemma":[0.9994043,0.0000594388,0.00007859652,0.0002242208,0.00007098965,0.0001625219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009303031,0.002148389,0.9900759,0.0001100635,0.0002162642,0.0003690665,0.0001234154,5.905112e-8,0.003453912,0.0001181376,0.001819557,0.001472135],"study_design_scores_gemma":[0.01815293,0.0006754351,0.8767763,0.0001064171,0.0006304642,0.0005608259,0.00007572059,0.00003913754,0.0995018,0.00008757261,0.003208673,0.0001847208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907097,0.001892096,0.00001113428,0.000222351,0.0002170214,0.000281426,0.00008082556,0.00007149127,0.006513971],"genre_scores_gemma":[0.9964738,0.0000665395,0.0009494631,0.0003068523,0.0005972591,0.00003780659,0.00009702511,0.00002333868,0.001447892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1132997,"threshold_uncertainty_score":0.5806241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695697149926651,"score_gpt":0.2457877624041352,"score_spread":0.2288307909048687,"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."}}