{"id":"W2905733110","doi":"10.1093/jamia/ocy143","title":"Designing a medication timeline for patients and physicians","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"Agency for Healthcare Research and Quality; University of Missouri; California Health Care Foundation","keywords":"Timeline; Computer science; Visualization; Process (computing); Multidisciplinary approach; Data visualization","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":[0.007349099,0.001044671,0.0003530431,0.0006674118,0.001193437,0.002588396,0.001411953,0.001610318,0.006653651],"category_scores_gemma":[0.02874918,0.0007558755,0.0006615606,0.0004935535,0.000854077,0.004073146,0.001815945,0.001123462,0.001245081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008602676,"about_ca_system_score_gemma":0.002272506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001019707,"about_ca_topic_score_gemma":0.001330645,"domain_scores_codex":[0.9950389,0.00285629,0.0004748767,0.0005812365,0.0008079758,0.0002406666],"domain_scores_gemma":[0.9813501,0.01044008,0.00177581,0.001555278,0.003640711,0.001237948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001882517,0.001364067,0.02301094,0.00344414,0.0001624398,0.001711754,0.03218226,0.008576958,0.07195233,0.01321321,0.04657173,0.7959276],"study_design_scores_gemma":[0.002660962,0.01141899,0.03802118,0.003193776,0.0006511398,0.006698931,0.01714191,0.08583049,0.09565528,0.02011129,0.7179195,0.0006965706],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1285552,0.0008129848,0.8375469,0.00508822,0.0005967452,0.002239701,0.0004929408,0.01432092,0.01034645],"genre_scores_gemma":[0.1939186,0.0003787345,0.7985035,0.0008495339,0.000175026,0.001348815,0.0004045462,0.000520418,0.003900746],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007349099,"threshold_uncertainty_score":0.03886622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916314979589402,"score_gpt":0.4016454906940264,"score_spread":0.3824823408981324,"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."}}