{"id":"W4385330648","doi":"10.1038/s41598-023-39271-7","title":"microRNA sequencing for biomarker detection in the diagnosis, classification and prognosis of Diffuse Large B Cell Lymphoma","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Lymphoma Diagnosis and Treatment","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"Canadian Institutes of Health Research; Eusko Jaurlaritza","keywords":"microRNA; Diffuse large B-cell lymphoma; Biomarker; Disease; Context (archaeology); Lymphoma; Bioinformatics; Pathogenesis; Cancer; Oncology; Medicine; Biology; Computational biology; Internal medicine; Gene; Genetics","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":[0.001360043,0.0003620379,0.0004682192,0.001386784,0.0002325997,0.0007134036,0.000211866,0.0003649079,0.0007173397],"category_scores_gemma":[0.002244232,0.0002189332,0.0003055621,0.0007383905,0.0002063798,0.0002964555,0.0003754801,0.0003486119,0.000264536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002822584,"about_ca_system_score_gemma":0.0003100049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004104645,"about_ca_topic_score_gemma":0.0007849019,"domain_scores_codex":[0.999175,0.0003265771,0.00008378568,0.000193866,0.0001555471,0.00006523984],"domain_scores_gemma":[0.9995147,0.0001983931,0.0001190453,0.00004676381,0.00007620079,0.00004483994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002898646,0.0002647598,0.4856995,0.0004803919,0.0005487074,0.0008744495,0.0004047489,0.002428456,0.3258161,0.0008192134,0.001610914,0.1781541],"study_design_scores_gemma":[0.0002500033,0.001991454,0.6389669,0.0003489579,0.001549838,0.007093368,0.0007659905,0.06635389,0.2485302,0.004447736,0.02959765,0.0001040318],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623986,0.01283187,0.02126277,0.0004519668,0.0001141589,0.0001259523,0.0009561192,0.000158172,0.001700371],"genre_scores_gemma":[0.9833789,0.001753219,0.01342267,0.0001872954,0.00004699119,0.00007857852,0.0005332166,0.00002342245,0.0005757451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001386784,"threshold_uncertainty_score":0.007192671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04519468028332955,"score_gpt":0.2908629120961467,"score_spread":0.2456682318128172,"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."}}