{"id":"W4394088294","doi":"10.6084/m9.figshare.22610374","title":"Additional file 1 of Personalized hypertension treatment recommendations by a data-driven model","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Data file; Personalized medicine; Database; Data science; Data mining; Bioinformatics; Biology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001419242,0.001445123,0.0009822872,0.001623786,0.0004282307,0.001388442,0.00180713,0.001720288,0.3892604],"category_scores_gemma":[0.01513902,0.0004859813,0.001084966,0.002300559,0.0002794064,0.0007693784,0.0006901293,0.001323228,0.08400942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144319,"about_ca_system_score_gemma":0.001660553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01057147,"about_ca_topic_score_gemma":0.02379869,"domain_scores_codex":[0.99934,0.0001452624,0.00008587426,0.0002285904,0.0001273228,0.00007284531],"domain_scores_gemma":[0.992654,0.005637551,0.000289379,0.0005653725,0.0006856919,0.0001680378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002393213,0.000105942,0.002812486,0.000879778,0.00006168487,0.00005338452,0.00001763449,0.001736252,0.00008417761,0.0004298291,0.9874698,0.006109776],"study_design_scores_gemma":[0.006091414,0.0004884934,0.02010666,0.001577228,0.0003343983,0.0006763153,0.0002338971,0.02464861,0.001872101,0.01219789,0.9316288,0.0001443456],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002242038,0.00002552395,0.0001300368,0.00006930833,0.00001866071,0.00002627015,0.9990916,0.0001715187,0.0002428666],"genre_scores_gemma":[0.003362464,0.00004782674,0.001547238,0.0001360215,0.00002764839,0.000317253,0.9930623,0.00007782408,0.001421377],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3892604,"threshold_uncertainty_score":0.8711459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1525576764168085,"score_gpt":0.3406591300443029,"score_spread":0.1881014536274944,"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."}}