{"id":"W4409329424","doi":"10.1016/j.crmeth.2025.101022","title":"Efficient and scalable construction of clinical variable networks for complex diseases with RAMEN","year":2025,"lang":"en","type":"article","venue":"Cell Reports Methods","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre; Lawson Health Research Institute; Western University; McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Scalability; Variable (mathematics); Computer science; Mathematics; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002954853,0.0001140217,0.0004117734,0.00007446663,0.0001362481,0.00005336135,0.0001643209,0.00008821439,0.000008689409],"category_scores_gemma":[0.0007666369,0.00009324315,0.00005709244,0.0003678283,0.0001596212,0.00003833141,0.000172502,0.00017506,6.743591e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001876966,"about_ca_system_score_gemma":0.0002774156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005994195,"about_ca_topic_score_gemma":6.257163e-7,"domain_scores_codex":[0.997889,0.0005394453,0.0007104232,0.0005106369,0.0001317992,0.0002186884],"domain_scores_gemma":[0.9968371,0.001713979,0.000497814,0.0006138071,0.0002208145,0.0001164223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001307529,0.0002541441,0.3322792,0.001063431,0.00007655134,0.00003172989,0.0001011027,0.1567675,0.0002167392,0.03834955,0.001031495,0.4696978],"study_design_scores_gemma":[0.0003361736,0.0001485368,0.02762732,0.00008274813,0.00004498607,0.00004534315,0.00001865776,0.9575322,0.0001701046,0.004212294,0.009671847,0.0001098151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01080254,0.0004328769,0.9844427,0.0001699176,0.0008213065,0.0004336403,0.000001214161,0.00007681076,0.002819007],"genre_scores_gemma":[0.07946975,0.00000925544,0.9199949,0.0001163098,0.0000434642,0.00002759258,0.000004133312,0.000007183335,0.0003274337],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8007646,"threshold_uncertainty_score":0.3802345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03829574484890023,"score_gpt":0.4172555522546955,"score_spread":0.3789598074057953,"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."}}