{"id":"W2095836161","doi":"10.1016/j.jbi.2014.05.004","title":"Usability study of clinical exome analysis software: Top lessons learned and recommendations","year":2014,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Child and Family Research Institute; University of British Columbia","funders":"","keywords":"Usability; Exome; Computer science; Exome sequencing; Coding (social sciences); Software; Human–computer interaction; Cognitive walkthrough; Data science; Pluralistic walkthrough; Software engineering; Mutation; Genetics; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.001513369,0.00007890062,0.0003650058,0.0001299802,0.00004831439,0.00001862239,0.0001730427,0.0001090905,0.0000217347],"category_scores_gemma":[0.001589855,0.00005801942,0.0001943306,0.0001973805,0.0001540966,0.000008857955,0.000113114,0.0001364016,7.677852e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005332687,"about_ca_system_score_gemma":0.00009445858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005771346,"about_ca_topic_score_gemma":0.000009285928,"domain_scores_codex":[0.9982284,0.0001174148,0.001269911,0.00007122759,0.0002093254,0.0001036916],"domain_scores_gemma":[0.998552,0.0001413339,0.0006719808,0.0002181616,0.0001996819,0.0002168504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005606703,0.007049153,0.3758554,0.0002688507,0.005620068,0.000009094522,0.00301586,0.0004360985,0.001731033,0.000130598,0.009295299,0.5960279],"study_design_scores_gemma":[0.01395563,0.02028064,0.6758319,0.0001184855,0.005452982,0.0001725438,0.02111054,0.01009744,0.001021091,0.002258974,0.2486496,0.001050141],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869247,0.00005178177,0.01215696,0.0006046543,0.0001441355,0.00005886728,0.00002200342,0.000001773089,0.00003510515],"genre_scores_gemma":[0.9934648,0.0003004787,0.005911533,0.0001541933,0.0001221444,8.188226e-7,0.00002678917,0.000003716512,0.00001557188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5949777,"threshold_uncertainty_score":0.2365963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04464179485604194,"score_gpt":0.3875980807749932,"score_spread":0.3429562859189513,"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."}}