{"id":"W4388076465","doi":"10.1016/j.cancergen.2023.08.060","title":"52. ClinGen Pediatric Cancer Taskforce initiatives to advance pediatric clinical interpretations through expert curation","year":2023,"lang":"en","type":"article","venue":"Cancer Genetics","topic":"Childhood Cancer Survivors' Quality of Life","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Context (archaeology); Disease; Medicine; Cancer; Pediatric cancer; Data curation; Data science; Pathology; Computer science; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000471541,0.0003511475,0.0006274875,0.0002506947,0.0001817517,0.00004722133,0.00030219,0.0002091574,0.0002678588],"category_scores_gemma":[0.0006776556,0.0003634456,0.0002351935,0.001765699,0.00009060468,0.0003140172,0.0001726846,0.0004262817,0.0002692924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000273573,"about_ca_system_score_gemma":0.0008425773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002920177,"about_ca_topic_score_gemma":0.0007732844,"domain_scores_codex":[0.9965613,0.0002002285,0.001181426,0.0008081273,0.0006668299,0.000582087],"domain_scores_gemma":[0.9976419,0.0005000412,0.0003622664,0.0006449197,0.0004536636,0.0003972224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004277092,0.0003100441,0.8194695,0.0006634977,0.0002739472,0.00003074991,0.03616081,0.005716047,0.0002418335,0.0001998192,0.1133675,0.02313862],"study_design_scores_gemma":[0.00421247,0.001262079,0.8250806,0.0004460811,0.001124886,0.000008866916,0.00308803,0.003055098,0.002147545,0.0007631387,0.1572374,0.001573774],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9455469,0.02694387,0.007937964,0.009527083,0.006827208,0.001439248,0.0003314643,0.0004944726,0.0009518492],"genre_scores_gemma":[0.7973241,0.1688223,0.005304251,0.01398971,0.01216616,0.0009008619,0.0001385814,0.0001508266,0.001203221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1482227,"threshold_uncertainty_score":0.9998817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.100964902491943,"score_gpt":0.4727871192913295,"score_spread":0.3718222167993865,"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."}}