{"id":"W4311014563","doi":"10.1093/ijpp/riac089.041","title":"Measuring drug name similarity to prioritise the application of tall-man lettering in a computerised pharmacy dispensing system","year":2022,"lang":"en","type":"article","venue":"International Journal of Pharmacy Practice","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Lettering; Pharmacy; Bigram; Family medicine; Trigram; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007350564,0.000158244,0.0003681982,0.0003507873,0.000445095,0.00003437961,0.001007165,0.00002070429,0.0000750048],"category_scores_gemma":[0.0006367741,0.0001453822,0.0001053677,0.0003382165,0.00002225965,0.0006985942,0.0004532684,0.001910401,0.00001944815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002587521,"about_ca_system_score_gemma":0.0009129237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003358674,"about_ca_topic_score_gemma":0.0001602391,"domain_scores_codex":[0.9933348,0.002875721,0.001799708,0.0002252879,0.001357949,0.0004065884],"domain_scores_gemma":[0.9942887,0.002009593,0.002361525,0.0002071536,0.0009742855,0.0001587335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01902015,0.003379947,0.3552367,0.004571311,0.002446715,0.002913854,0.1227103,0.06435204,0.2318524,0.007518398,0.02048328,0.165515],"study_design_scores_gemma":[0.01069595,0.0001057493,0.004736104,0.001073179,0.0001302671,0.001625391,0.01063098,0.05817673,0.001188264,0.00004676558,0.9112488,0.0003418235],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9367023,0.0004370511,0.01522289,0.04004132,0.004877903,0.001566702,0.00003027721,0.00003941379,0.001082113],"genre_scores_gemma":[0.9931297,0.00005706827,0.001350214,0.004143047,0.001141672,0.0001117301,0.000004028864,0.00003281939,0.00002968058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8907655,"threshold_uncertainty_score":0.8299847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09751551632708398,"score_gpt":0.47836435616258,"score_spread":0.380848839835496,"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."}}