{"id":"W2118414897","doi":"10.1007/978-3-642-38326-7_3","title":"Using Constraint Logic Programming to Implement Iterative Actions and Numerical Measures during Mitigation of Concurrently Applied Clinical Practice Guidelines","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Clinical practice guidelines implementation","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Children's Hospital of Eastern Ontario; McGill University; University of Ottawa","funders":"","keywords":"Computer science; Clinical Practice; Logic programming; Process (computing); Constraint (computer-aided design); Constraint logic programming; Iterative and incremental development; Software engineering; Machine learning; Constraint satisfaction; Algorithm; Artificial intelligence; Programming language; Medicine; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002475158,0.0003594877,0.0007479868,0.0003674574,0.0001705205,0.0001387623,0.0002081211,0.0002189448,0.00006167622],"category_scores_gemma":[0.008427171,0.0003133904,0.0001174729,0.0002982374,0.0005629116,0.0003339449,0.0003512188,0.0007490737,0.00000733356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002744964,"about_ca_system_score_gemma":0.0007377917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008657004,"about_ca_topic_score_gemma":0.00002964319,"domain_scores_codex":[0.9952721,0.00008712913,0.002377065,0.0009614318,0.0009285607,0.000373716],"domain_scores_gemma":[0.9939006,0.002187152,0.00107554,0.0004127352,0.002144562,0.0002794007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001500361,0.0001189967,0.0004701235,0.0001101517,0.00009410712,0.00002179749,0.0005191086,0.002657651,0.002985584,0.002031954,0.00004012335,0.9908004],"study_design_scores_gemma":[0.03623469,0.01645159,0.02313235,0.01789112,0.007133696,0.007239771,0.001140632,0.6464156,0.04056141,0.07037515,0.1228658,0.01055822],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005892478,0.0001362668,0.985599,0.00551428,0.0006011592,0.001924115,0.00001667198,0.0000408291,0.0002751332],"genre_scores_gemma":[0.332636,0.00003615265,0.6638406,0.002857178,0.0005528935,0.00002208591,0.00002334785,0.00002328129,0.000008518084],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9802421,"threshold_uncertainty_score":0.9999318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3518250663271454,"score_gpt":0.5217212558944098,"score_spread":0.1698961895672643,"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."}}