{"id":"W975601493","doi":"10.1007/978-3-319-21145-9_13","title":"Using Graph Transformations for Formalizing Prescriptions and Monitoring Adherence","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; University of British Columbia","funders":"","keywords":"Medical prescription; Computer science; Semantics (computer science); Intervention (counseling); Graph; Medicine; Theoretical computer science; Nursing; Programming language","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":[0.003327997,0.001259162,0.0007933956,0.00169203,0.0009943225,0.002681956,0.001997215,0.001681826,0.00441114],"category_scores_gemma":[0.01480216,0.001092423,0.002541059,0.001422872,0.003878385,0.004645267,0.003145477,0.004254203,0.000920439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001433681,"about_ca_system_score_gemma":0.002581938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004758507,"about_ca_topic_score_gemma":0.006199982,"domain_scores_codex":[0.9938934,0.002152961,0.0005826046,0.001117869,0.00179306,0.0004600805],"domain_scores_gemma":[0.9900017,0.005837902,0.0007444013,0.002219296,0.001038619,0.000157997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001484618,0.0001365385,0.001181799,0.0003224286,0.00008434764,0.0005570254,0.001191595,0.07028743,0.01109163,0.7885517,0.002695851,0.1237512],"study_design_scores_gemma":[0.00006299878,0.00007763747,0.0003056552,0.0001441702,0.0001602984,0.0002703931,0.0002168385,0.3148722,0.02552137,0.6387211,0.01957144,0.00007582823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004154151,0.00003679066,0.9921022,0.0001443646,0.00004293432,0.0000889033,0.00007206807,0.001377922,0.00198074],"genre_scores_gemma":[0.23004,0.0001995141,0.7635713,0.0002402132,0.00006568436,0.0002542911,0.0003131727,0.001110181,0.004205565],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004758507,"threshold_uncertainty_score":0.01760036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07818860292628553,"score_gpt":0.3011210742802441,"score_spread":0.2229324713539586,"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."}}