{"id":"W4409628583","doi":"10.1158/1538-7445.am2025-5043","title":"Abstract 5043: reVUE: repository for variants with unexpected effects","year":2025,"lang":"en","type":"article","venue":"Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Biology; Medicine; Computational biology; Genetics","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":[0.004605421,0.002842712,0.002319599,0.006282637,0.0008777831,0.003728012,0.006595155,0.003054153,0.05816837],"category_scores_gemma":[0.0189298,0.001472273,0.001737048,0.005583781,0.0006747655,0.002420355,0.004721252,0.002228634,0.04415419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001208744,"about_ca_system_score_gemma":0.002711818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004229494,"about_ca_topic_score_gemma":0.006299634,"domain_scores_codex":[0.9975145,0.0004333856,0.0004397885,0.0007430942,0.0006679973,0.0002012142],"domain_scores_gemma":[0.9906023,0.004372032,0.001178577,0.002035799,0.001017198,0.0007941904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002705908,0.0001863117,0.01558662,0.009066076,0.0005779877,0.002563251,0.0005675844,0.005911748,0.01325771,0.01033945,0.8626744,0.07656289],"study_design_scores_gemma":[0.001664276,0.0002780143,0.01233135,0.001324163,0.000454563,0.003006436,0.000239978,0.009393599,0.01967094,0.01452437,0.9366486,0.0004637588],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.004362437,0.002228303,0.02020745,0.0006433402,0.0003815789,0.000256789,0.8893676,0.0767249,0.005827563],"genre_scores_gemma":[0.01901624,0.001271849,0.02349175,0.0008271756,0.0001579673,0.0006497184,0.935439,0.01648574,0.002660578],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05816837,"threshold_uncertainty_score":0.1945926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02651702144814017,"score_gpt":0.4173939907165293,"score_spread":0.3908769692683892,"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."}}