{"id":"W4311845006","doi":"10.1136/jmg-2022-108807","title":"Optimising clinical care through <i>CDH1</i>-specific germline variant curation: improvement of clinical assertions and updated curation guidelines","year":2022,"lang":"en","type":"article","venue":"Journal of Medical Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"National Health and Medical Research Council; National Institutes of Health; Universidade do Porto; National Human Genome Research Institute; University of California, Irvine; Radboud Universiteit","keywords":"Data curation; Germline; Computational biology; Computer science; Genetics; Data science; Biology; Medicine; Bioinformatics; Gene","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":[0.08575886,0.001116589,0.001250514,0.006317725,0.002357709,0.008459674,0.005759853,0.004913594,0.003886939],"category_scores_gemma":[0.2174279,0.0009276877,0.00202174,0.003063345,0.003154223,0.006488872,0.008343318,0.009829837,0.003130342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00852926,"about_ca_system_score_gemma":0.03385098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02059777,"about_ca_topic_score_gemma":0.02671371,"domain_scores_codex":[0.9322526,0.03223008,0.01401308,0.004218196,0.01507157,0.002214433],"domain_scores_gemma":[0.7380599,0.08832413,0.02085962,0.01587623,0.1242465,0.01263354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001326635,0.0002961809,0.02479936,0.002814892,0.0001902351,0.001246626,0.004116566,0.003069988,0.0020903,0.01643046,0.4069316,0.5378811],"study_design_scores_gemma":[0.0002772617,0.0003471195,0.02350162,0.01964525,0.0003605551,0.003723063,0.004353078,0.008084367,0.005346045,0.0487246,0.8852021,0.0004349058],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.01871039,0.06052239,0.2176163,0.6109914,0.01081133,0.002694914,0.002152061,0.005151494,0.07134966],"genre_scores_gemma":[0.1541449,0.04659422,0.6099776,0.156091,0.008784221,0.002671834,0.006580482,0.002677697,0.0124781],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.08575886,"threshold_uncertainty_score":0.4535416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07879177397429732,"score_gpt":0.4244734466440226,"score_spread":0.3456816726697253,"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."}}