{"id":"W2061694322","doi":"10.2196/ijmr.2089","title":"Improving Interoperability in ePrescribing","year":2012,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interoperability; Statement (logic); Computer science; Information retrieval; World Wide Web; Linguistics; Philosophy","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":[0.05666916,0.0004201517,0.0005258707,0.003687844,0.001781699,0.005341614,0.001856937,0.001401865,0.002720731],"category_scores_gemma":[0.1444824,0.0003142072,0.0007936104,0.004216,0.003453332,0.008123341,0.006156609,0.00167868,0.0005176806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00358582,"about_ca_system_score_gemma":0.006717168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002063821,"about_ca_topic_score_gemma":0.001165234,"domain_scores_codex":[0.9271836,0.04346103,0.01023861,0.00207447,0.01524176,0.001800596],"domain_scores_gemma":[0.7509599,0.1618095,0.03395779,0.02584678,0.02478703,0.002638929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002900377,0.001263033,0.2048054,0.002370504,0.0001489621,0.0007768833,0.02418916,0.004614687,0.004297718,0.05735538,0.004242276,0.695646],"study_design_scores_gemma":[0.0002976353,0.003016777,0.4860353,0.008262953,0.0004757026,0.004886537,0.05185569,0.02454431,0.03976517,0.1399996,0.2404211,0.0004391593],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7776492,0.004481589,0.1316201,0.02183586,0.0003029968,0.001303594,0.0003156663,0.0006945482,0.06179647],"genre_scores_gemma":[0.9108045,0.001357744,0.08424598,0.0009794564,0.000132313,0.000367096,0.0003394675,0.00006754942,0.001705811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05666916,"threshold_uncertainty_score":0.2996988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2614680874545345,"score_gpt":0.6153051031339899,"score_spread":0.3538370156794554,"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."}}