{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007325483,0.000124889,0.0004950106,0.0001895552,0.0003021531,0.00001153314,0.0002914968,0.0001707725,0.00001601659],"category_scores_gemma":[0.06327403,0.0000704787,0.00007421564,0.0006652713,0.00008309902,0.0002025876,0.0001092289,0.0009737925,0.00001532567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001949336,"about_ca_system_score_gemma":0.002304211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001396843,"about_ca_topic_score_gemma":0.000240779,"domain_scores_codex":[0.9947377,0.0007337074,0.002595055,0.00007105042,0.001357897,0.0005045862],"domain_scores_gemma":[0.9863347,0.005371478,0.006602861,0.0001979693,0.000905784,0.0005871952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001606137,0.00005325504,0.9753328,0.00006466077,0.00003220672,4.562501e-7,0.00159528,4.886057e-7,0.000003955538,0.00002582754,0.0178708,0.005004222],"study_design_scores_gemma":[0.001992302,0.0002522569,0.9717962,0.001118219,0.00003200446,8.756095e-7,0.00080684,0.0002013308,0.00000565356,0.0001390098,0.02354905,0.000106274],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801143,0.00002326846,0.0004606677,0.01768684,0.0008308931,0.0004418326,0.000158408,0.00001137285,0.0002724626],"genre_scores_gemma":[0.9952813,0.0004229395,0.0001603073,0.003482012,0.0005281701,0.0000145661,0.000006776734,0.00001321239,0.00009073618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05594854,"threshold_uncertainty_score":0.9446164,"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."}}