{"id":"W4380625864","doi":"10.1093/ndt/gfad063c_3815","title":"#3815 A DEEP LEARNING APPROACH TO PERSONALISED ANTI-HYPERTENSIVE MEDICATION TITRATION","year":2023,"lang":"en","type":"article","venue":"Nephrology Dialysis Transplantation","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Western University","funders":"","keywords":"Medicine; Blood pressure; Clinical decision support system; Clinical trial; Placebo; Randomized controlled trial; Stroke (engine); Physical therapy; Emergency medicine; Internal medicine; Decision support system; Artificial intelligence","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":[0.000687515,0.0001768452,0.0003039598,0.0005688393,0.0003251538,0.00007170928,0.0004418748,0.0001902517,0.00001684283],"category_scores_gemma":[0.000136485,0.0001809912,0.00008970495,0.001523886,0.00004115703,0.000335045,0.00002910711,0.0003306114,0.0003341308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004884143,"about_ca_system_score_gemma":0.00006491911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001702675,"about_ca_topic_score_gemma":0.00007256021,"domain_scores_codex":[0.9976497,0.0005494872,0.0003411951,0.0006367917,0.0004399429,0.000382887],"domain_scores_gemma":[0.9989294,0.0002814866,0.0001376593,0.0003196417,0.0001875062,0.0001443101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003101744,0.0003207643,0.06419323,0.0007205257,0.0005157727,0.000182561,0.1485008,0.5363308,0.04860908,0.03927382,0.002441101,0.1586014],"study_design_scores_gemma":[0.0006731567,0.0001469,0.267892,0.00001454503,0.0001098927,0.00005691046,0.0002290598,0.7278956,0.0002336486,0.0002476528,0.00222529,0.0002753719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3513993,0.00004007259,0.6404352,0.005887335,0.0002239557,0.0003604453,0.000003408772,0.0007976361,0.0008526735],"genre_scores_gemma":[0.9806384,0.0001673682,0.0156322,0.002527315,0.000156871,0.0001482204,0.0006392483,0.00001894733,0.00007139619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6292391,"threshold_uncertainty_score":0.7380608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01898786435956119,"score_gpt":0.270812173149692,"score_spread":0.2518243087901308,"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."}}