{"id":"W2044819930","doi":"10.3390/pharmacy2040260","title":"Optimizing Effectiveness in Electronic Prescriptions for Pediatric Outpatients: A Call for Responsive Action","year":2014,"lang":"en","type":"article","venue":"Pharmacy","topic":"Pharmaceutical studies and practices","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Health Services","funders":"","keywords":"Medical prescription; SAFER; Medicine; Pharmacotherapy; Medical emergency; Health care; Patient safety; White paper; Electronic prescribing; Government (linguistics); Family medicine; Intensive care medicine; Nursing; Computer science; Computer security","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":[0.0006877754,0.0001202028,0.000221774,0.000104329,0.0001339507,0.00001545239,0.00005561046,0.00002907458,0.00001678254],"category_scores_gemma":[0.001062094,0.0001077944,0.00009904469,0.0001383422,0.00001664064,0.0001938082,0.00002566385,0.0002039624,0.000009203169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001845213,"about_ca_system_score_gemma":0.00006851983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003561518,"about_ca_topic_score_gemma":0.00002594512,"domain_scores_codex":[0.9989297,0.0001458894,0.0001885868,0.0002416554,0.000106091,0.0003880472],"domain_scores_gemma":[0.9979669,0.001634936,0.00008025649,0.0000877989,0.0001371784,0.00009293306],"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":[0.2082833,0.007436833,0.1610414,0.0131363,0.00200189,0.00002152014,0.007660424,0.002338984,0.2026021,0.006994176,0.02931166,0.3591715],"study_design_scores_gemma":[0.02263787,0.00141994,0.009794383,0.00007656626,0.001085683,0.000008166638,0.00009694472,0.03255023,0.01641037,0.0009775454,0.9146028,0.0003395646],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9280193,0.002912435,0.05823314,0.003469341,0.001045809,0.005193653,0.00004221737,0.0001137927,0.0009702758],"genre_scores_gemma":[0.9959242,0.0008988493,0.0008040555,0.0006142501,0.0004947897,0.001081529,0.00002224616,0.00002283579,0.0001372216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8852911,"threshold_uncertainty_score":0.4395728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1044688021661316,"score_gpt":0.444308546433136,"score_spread":0.3398397442670044,"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."}}