{"id":"W3213053655","doi":"10.32614/cran.package.drugprepr","title":"drugprepr: Prepare Electronic Prescription Record Data to Estimate Drug Exposure","year":2021,"lang":"en","type":"dataset","venue":"","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Open Text (Canada)","funders":"","keywords":"Medical prescription; Drug; Prescription drug; Computer science; Medicine; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001874472,0.001295804,0.001070801,0.002793115,0.0004876765,0.001714817,0.002517437,0.001738214,0.07030181],"category_scores_gemma":[0.01167751,0.0008429294,0.001310617,0.004486557,0.0002775622,0.001249825,0.002020032,0.001851707,0.0703674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001678968,"about_ca_system_score_gemma":0.002460778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02992426,"about_ca_topic_score_gemma":0.05264112,"domain_scores_codex":[0.9987161,0.0002789455,0.0002530474,0.0003236432,0.0002654479,0.0001628514],"domain_scores_gemma":[0.995849,0.0009978837,0.0008411804,0.0009037424,0.0009913649,0.0004167126],"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":[0.0001326121,0.0000388326,0.004240008,0.0005830184,0.00005763362,0.00002194733,0.00002526175,0.0002342323,0.00008272666,0.0005608803,0.9912349,0.002787937],"study_design_scores_gemma":[0.001337362,0.00007858581,0.03017594,0.0007150483,0.0001138401,0.000109692,0.0001591024,0.001308649,0.0007820978,0.002317588,0.9628262,0.00007594255],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002223331,0.00002595106,0.00008803829,0.0001140509,0.00001754674,0.00002062314,0.9987776,0.0001892574,0.0005446108],"genre_scores_gemma":[0.0008067575,0.00003573744,0.0004791791,0.0001154699,0.00001089466,0.0001174139,0.9976908,0.0000493709,0.0006944121],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07030181,"threshold_uncertainty_score":0.2351829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1401939356899322,"score_gpt":0.4480135518360909,"score_spread":0.3078196161461586,"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."}}