{"id":"W4414526368","doi":"10.1101/2025.09.23.678112","title":"Screen-VarCal: An Interpretable Probabilistic Framework for Recalibrating ACMG Rule-Based Variant Classification in Preventive Medicine","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Drive & Gear (Canada)","funders":"","keywords":"Probabilistic logic; False positive paradox; Heuristics; Zygosity; Logistic regression; Medical genetics; Task (project management); Context (archaeology)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002950268,0.0006716104,0.0009153329,0.0006816073,0.0002562503,0.0003782121,0.002586687,0.000899213,0.00002381991],"category_scores_gemma":[0.005480011,0.0006974603,0.0001421772,0.001385478,0.0001464959,0.0004436284,0.0008682254,0.001999173,0.000005548202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006540228,"about_ca_system_score_gemma":0.002072504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000459007,"about_ca_topic_score_gemma":0.00002572897,"domain_scores_codex":[0.9940753,0.001132851,0.001260912,0.002171871,0.0005500671,0.0008090078],"domain_scores_gemma":[0.993516,0.00139222,0.0009334349,0.003029245,0.0007959754,0.0003331496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003549228,0.001151824,0.03270631,0.01062028,0.0002488223,0.0001050868,0.0005547001,0.0176252,0.02275395,0.9130453,0.0003246839,0.0005088803],"study_design_scores_gemma":[0.0008046192,0.000321657,0.05025608,0.008343608,0.00007254031,2.179835e-8,0.00001057104,0.9341115,0.002594015,0.002023482,0.0005241961,0.0009376665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01726892,0.0003431503,0.9727629,0.004091882,0.001482674,0.003043163,0.0001441377,0.0008355548,0.00002762748],"genre_scores_gemma":[0.6219789,0.00001241835,0.3759839,0.0005904409,0.0003181628,0.001048248,0.000002501839,0.00006042859,0.000004973554],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9164863,"threshold_uncertainty_score":0.9995477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03266303954590395,"score_gpt":0.3058877594893517,"score_spread":0.2732247199434477,"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."}}