{"id":"W4280538810","doi":"10.1038/s41598-022-12445-5","title":"NMR-based metabolomic profiling can differentiate follicular lymphoma from benign lymph node tissues and may be predictive of outcome","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"Alberta Precision Laboratories","keywords":"Metabolomics; Follicular lymphoma; Lymphoma; Lymph node; Medicine; Pathology; Oncology; Computational biology; Biology; Internal medicine; Bioinformatics","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":[0.0003362503,0.0002533759,0.0001782796,0.0009195143,0.0001329865,0.00035896,0.0001020018,0.0002252657,0.0009721979],"category_scores_gemma":[0.0008744044,0.00005616584,0.0001634591,0.0003421168,0.0001929665,0.0001883469,0.0001831822,0.0001850268,0.0001771565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001508634,"about_ca_system_score_gemma":0.00007539782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006034747,"about_ca_topic_score_gemma":0.0007353331,"domain_scores_codex":[0.9998864,0.00003402227,0.00001058528,0.00002748877,0.00002062898,0.00002082244],"domain_scores_gemma":[0.9996836,0.0001164346,0.0000988688,0.00002095914,0.00004092556,0.00003923248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001557781,0.0001558961,0.7404172,0.00007070993,0.0001263864,0.0005163581,0.0001151392,0.001017525,0.2227663,0.0001228283,0.000312688,0.03282126],"study_design_scores_gemma":[0.00002116596,0.000750781,0.9454902,0.00001642024,0.0001094161,0.001485083,0.0003489342,0.01097806,0.03933892,0.0006748364,0.0007654472,0.0000206623],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972889,0.0002258729,0.001821718,0.00004672683,0.000004706329,0.00001123317,0.0002285459,0.0000260746,0.0003461286],"genre_scores_gemma":[0.9990108,0.00005433775,0.0006647073,0.00001208739,0.000005831377,0.000005456054,0.0001565458,0.000003095362,0.0000870414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009721979,"threshold_uncertainty_score":0.003252387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443397106976409,"score_gpt":0.2492453268906027,"score_spread":0.2348113558208387,"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."}}