{"id":"W2804424029","doi":"10.4212/cjhp.v50i4.2065","title":"PharmaNet Database and Seamless Care","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Hospital Pharmacy","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Database; Computer science; World Wide Web; Information retrieval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005703281,0.0004353008,0.0007154932,0.003503482,0.000844339,0.006621397,0.002135869,0.001256501,0.04507007],"category_scores_gemma":[0.02508928,0.0006346878,0.0007217956,0.004947016,0.0005664439,0.008127077,0.004092471,0.001333574,0.01476997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001938192,"about_ca_system_score_gemma":0.004952439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008374634,"about_ca_topic_score_gemma":0.005592783,"domain_scores_codex":[0.9935416,0.001938166,0.001246418,0.001048024,0.001794733,0.0004310073],"domain_scores_gemma":[0.9833379,0.005703875,0.001528928,0.00574312,0.002077141,0.001608899],"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":[0.002394814,0.0004624144,0.02620165,0.0007021942,0.0002083071,0.0005133278,0.000513249,0.003453805,0.001499628,0.1321681,0.4975166,0.3343658],"study_design_scores_gemma":[0.0005582144,0.0001817007,0.01108283,0.0004144076,0.0001339235,0.001110305,0.0003885788,0.03301486,0.004774513,0.06935764,0.8788645,0.0001185191],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0504039,0.008614613,0.2495033,0.04333889,0.003317935,0.001588159,0.3124835,0.07572671,0.2550231],"genre_scores_gemma":[0.4614202,0.005186086,0.1918455,0.008685394,0.001881496,0.0008045005,0.2619855,0.005826209,0.06236506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04507007,"threshold_uncertainty_score":0.1507744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0554223249841775,"score_gpt":0.4350967879128372,"score_spread":0.3796744629286597,"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."}}