{"id":"W4282960066","doi":"10.1158/1538-7445.am2022-1193","title":"Abstract 1193: Enhancing pediatric cancer variant curation and representation through standardized classification and automation","year":2022,"lang":"en","type":"article","venue":"Cancer Research","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Context (archaeology); Terminology; Data curation; Disease; Cancer; Pediatric cancer; Medicine; Data science; Computer science; Biology; Pathology; Internal medicine","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":[0.0333128,0.001314783,0.001177993,0.01268275,0.001437463,0.008359703,0.003044891,0.001361096,0.005183995],"category_scores_gemma":[0.08035035,0.0008785253,0.002699165,0.007603628,0.001101097,0.004997957,0.008959162,0.002803239,0.00560553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002712562,"about_ca_system_score_gemma":0.0109111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0160078,"about_ca_topic_score_gemma":0.01372839,"domain_scores_codex":[0.9650151,0.01066863,0.007280717,0.00566346,0.01019078,0.001181344],"domain_scores_gemma":[0.9197875,0.02072859,0.007010541,0.02555676,0.02492423,0.001992436],"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":[0.0005840093,0.0002972222,0.04228229,0.002259577,0.0005448824,0.00087574,0.002996693,0.01047014,0.02382104,0.03870103,0.2914693,0.585698],"study_design_scores_gemma":[0.0002302645,0.0002565953,0.02995839,0.001807238,0.0004433223,0.001249008,0.001259602,0.1214913,0.06692617,0.06292227,0.7131079,0.0003480333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01579108,0.0008567212,0.83656,0.003904429,0.0004975337,0.001798472,0.03332914,0.09700202,0.01026069],"genre_scores_gemma":[0.05500471,0.0006056396,0.8604704,0.001248349,0.0002220383,0.0009743457,0.07288972,0.005959371,0.00262545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0333128,"threshold_uncertainty_score":0.176177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06046935526973776,"score_gpt":0.4049935783996543,"score_spread":0.3445242231299165,"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."}}