{"id":"W2563006377","doi":"10.1093/jamia/ocw169","title":"Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; The Quebec Population Health Research Network","funders":"Agency for Healthcare Research and Quality","keywords":"Respondent; Test (biology); Computer science; Health literacy; Numeracy; Graphics; Table (database); Affect (linguistics); Perception; Psychology; Health care; Literacy; Data mining; Communication; Computer graphics (images)","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":[0.001285127,0.0007009696,0.0002867316,0.0005412503,0.0002389747,0.001267864,0.0004030992,0.0009025034,0.02078582],"category_scores_gemma":[0.01986248,0.000223123,0.0005118369,0.0002764751,0.0002790465,0.001523064,0.0009573254,0.0006080979,0.001831717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002127179,"about_ca_system_score_gemma":0.0002424916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003155903,"about_ca_topic_score_gemma":0.000548179,"domain_scores_codex":[0.998998,0.0006421974,0.00008112846,0.0000731556,0.0001340978,0.00007142989],"domain_scores_gemma":[0.9908201,0.006522144,0.001646244,0.0002711311,0.0003889864,0.0003514329],"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":[0.007154047,0.002254678,0.1491576,0.002437293,0.0002144481,0.0008772761,0.004270734,0.002091506,0.02339686,0.001768101,0.04828968,0.7580878],"study_design_scores_gemma":[0.003837234,0.02422616,0.676585,0.003959374,0.001654626,0.008405778,0.01440802,0.02119926,0.05578114,0.01544502,0.1736112,0.0008871926],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9272085,0.004251787,0.02252765,0.008929862,0.0004587948,0.0005173365,0.00154363,0.003474441,0.03108785],"genre_scores_gemma":[0.9725596,0.001594695,0.02101656,0.001700473,0.0002830748,0.0001660981,0.0003662446,0.00009125769,0.002221815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02078582,"threshold_uncertainty_score":0.06953543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01903462415663413,"score_gpt":0.3618722026524899,"score_spread":0.3428375784958558,"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."}}