{"id":"W2832938734","doi":"10.1186/s13075-018-1634-8","title":"Hydroxychloroquine prescription trends and predictors for excess dosing per recent ophthalmology guidelines","year":2018,"lang":"en","type":"article","venue":"Arthritis Research & Therapy","topic":"Drug-Induced Ocular Toxicity","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Calgary; Toronto Western Hospital; Research Canada","funders":"National Center for Advancing Translational Sciences; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health","keywords":"Hydroxychloroquine; Medicine; Dosing; Medical prescription; Rheumatology; Internal medicine; Ophthalmology; Emergency medicine; Coronavirus disease 2019 (COVID-19); Pharmacology","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":[0.001969405,0.0002115379,0.0003792628,0.0004539987,0.0003918423,0.00008556929,0.0001793496,0.0001972209,0.0007073883],"category_scores_gemma":[0.0006174639,0.0001832461,0.00009175584,0.0004427625,0.0006414504,0.0002391532,0.0000939383,0.000341216,0.00002961741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001062089,"about_ca_system_score_gemma":0.0001160464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001505604,"about_ca_topic_score_gemma":0.00007074302,"domain_scores_codex":[0.9972021,0.0002893062,0.0004352435,0.0006151088,0.0006927787,0.0007654653],"domain_scores_gemma":[0.9976158,0.0001509648,0.00006335496,0.0005083377,0.001348748,0.0003127526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009919223,0.0002428546,0.005432324,0.00002477659,0.0001034206,0.00005112785,0.0006924795,6.335281e-7,0.1695226,0.0001071607,0.006546418,0.8162843],"study_design_scores_gemma":[0.01554071,0.01010713,0.09448746,0.0005492977,0.0000279481,0.001527502,0.0003989869,0.00179033,0.1077108,0.002738917,0.7645405,0.0005803989],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790752,0.006846761,0.0002311365,0.008979462,0.0003837739,0.000945055,0.00002855535,0.0000876899,0.003422366],"genre_scores_gemma":[0.9825844,0.01019366,0.00280127,0.0004077836,0.001546552,0.000224525,0.000072291,0.00006850481,0.002101069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8157039,"threshold_uncertainty_score":0.7745406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1584634784432552,"score_gpt":0.439545684033783,"score_spread":0.2810822055905278,"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."}}