{"id":"W2904745162","doi":"10.1093/jamia/ocy153","title":"Development and user evaluation of a rare disease gene prioritization workflow based on cognitive ergonomics","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Genome British Columbia; BC Children's Hospital; Michael Smith Health Research BC; BC Children’s Hospital Foundation; Children's Hospital Foundation; Canadian Institutes of Health Research; Genome Canada","keywords":"Workflow; Computer science; Prioritization; Cognition; Exome sequencing; Precision medicine; Artificial intelligence; Data science; Machine learning; Bioinformatics; Medicine; Phenotype; Gene; Genetics; Biology; Database; Management science; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01242234,0.001483831,0.0006373003,0.0007691958,0.000577249,0.001794842,0.00221228,0.00111837,0.003808656],"category_scores_gemma":[0.02715352,0.0005148582,0.0007380887,0.00036803,0.0006775405,0.001100739,0.001450788,0.0006805511,0.0008535279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009552253,"about_ca_system_score_gemma":0.001809464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002443967,"about_ca_topic_score_gemma":0.001868073,"domain_scores_codex":[0.9953934,0.002822875,0.0004393477,0.0006231011,0.0004897916,0.0002314383],"domain_scores_gemma":[0.9765968,0.01594732,0.0008668271,0.002205113,0.003201809,0.001182134],"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":[0.02222875,0.01800099,0.09833225,0.004694352,0.000673543,0.003166643,0.03561648,0.06781657,0.1551546,0.002787339,0.01050009,0.5810285],"study_design_scores_gemma":[0.007588609,0.03901275,0.1130245,0.00132745,0.001091353,0.003624882,0.01292126,0.6337088,0.1424903,0.006868476,0.03701756,0.001324184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8734127,0.00008966448,0.1161269,0.0002267333,0.00004538037,0.00288818,0.0005254683,0.005186735,0.001498167],"genre_scores_gemma":[0.7488021,0.00008844939,0.2467985,0.0001678319,0.00001369875,0.001963076,0.0009643841,0.0002482634,0.0009537696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01242234,"threshold_uncertainty_score":0.06569636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008921778719840325,"score_gpt":0.2672990010688626,"score_spread":0.2583772223490222,"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."}}