{"id":"W2940825203","doi":"10.3233/978-1-61499-951-5-303","title":"Uncovering the Mysteries of Electronic Medication Reconciliation","year":2019,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Island Health","funders":"","keywords":"Medical prescription; Medication Reconciliation; Process (computing); Electronic health record; Health records; Internet privacy; Public relations; Nursing; Business; Medicine; Computer science; Political science; Health care; Law; Pharmacist; Pharmacy","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":[0.03505884,0.0005145594,0.0004844218,0.002743875,0.01015875,0.01576627,0.002474065,0.0040699,0.003244526],"category_scores_gemma":[0.09069207,0.00107544,0.0005828387,0.002511039,0.0280086,0.03379942,0.00948424,0.01013223,0.0006410222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005845567,"about_ca_system_score_gemma":0.01353055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006713122,"about_ca_topic_score_gemma":0.01129678,"domain_scores_codex":[0.9718043,0.01878311,0.001254952,0.001731383,0.00507887,0.001347371],"domain_scores_gemma":[0.863474,0.1092099,0.007171593,0.008852701,0.008575652,0.002716041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000205773,0.0002098249,0.02791905,0.0015963,0.00009682336,0.002243662,0.3903592,0.00148836,0.001760305,0.3145087,0.05072292,0.2088892],"study_design_scores_gemma":[0.00003939994,0.0001381519,0.01040577,0.003275435,0.00006329892,0.002341144,0.3965289,0.006859808,0.002205027,0.3014007,0.2765331,0.0002092689],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2961802,0.01698759,0.06471372,0.5520889,0.002335596,0.0003231966,0.0004339953,0.0005051832,0.06643169],"genre_scores_gemma":[0.9192514,0.009653039,0.04681384,0.01740345,0.0007896999,0.0001828468,0.0001812076,0.0002307839,0.005493719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03505884,"threshold_uncertainty_score":0.1854111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05402626011821232,"score_gpt":0.4385907308946618,"score_spread":0.3845644707764495,"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."}}