{"id":"W4402863797","doi":"10.3390/cancers16193278","title":"Proteomic Profiling Identifies Predictive Signatures for Progression Risk in Patients with Advanced-Stage Follicular Lymphoma","year":2024,"lang":"en","type":"article","venue":"Cancers","topic":"Lymphoma Diagnosis and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Karen Elise Jensens Fond; Aarhus Universitet","keywords":"Follicular lymphoma; Stage (stratigraphy); Profiling (computer programming); Medicine; Follicular phase; Oncology; Lymphoma; Computational biology; Internal medicine; Bioinformatics; Computer science; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002994867,0.0002270639,0.0002733665,0.0008652741,0.0002222336,0.0004949945,0.000153605,0.0003312862,0.0006907896],"category_scores_gemma":[0.0006965861,0.00009249608,0.0001920833,0.0004803881,0.0001646629,0.0002238543,0.0002664316,0.0003050853,0.0001737541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000182974,"about_ca_system_score_gemma":0.0001380016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005574535,"about_ca_topic_score_gemma":0.0005922184,"domain_scores_codex":[0.9998901,0.00001825334,0.00001114947,0.00002700634,0.00003052203,0.00002292273],"domain_scores_gemma":[0.9997754,0.00005409335,0.00008121428,0.00001147665,0.00003809781,0.00003976186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001473901,0.00009047038,0.9298187,0.000046571,0.0000703999,0.0005027347,0.0001483109,0.0003589006,0.05337468,0.00004645014,0.0002568124,0.01381207],"study_design_scores_gemma":[0.00002965666,0.00033106,0.9887706,0.000008620143,0.00006146755,0.001649121,0.0002043661,0.002627479,0.005570994,0.0001153626,0.0006243258,0.000006831252],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992926,0.0002469857,0.0001943422,0.00002141442,0.000002470272,0.000005123234,0.0000794183,0.000007183606,0.0001504909],"genre_scores_gemma":[0.9994389,0.00006628605,0.0002376823,0.00001024709,0.000006454095,0.000003869889,0.0001741844,0.0000012838,0.00006102365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008652741,"threshold_uncertainty_score":0.002310932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005972184959994875,"score_gpt":0.2694780328920639,"score_spread":0.263505847932069,"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."}}