{"id":"W4391360005","doi":"10.1542/hpeds.2023-007418","title":"An Algorithm to Assess Guideline Concordance of Antibiotic Choice in Community-Acquired Pneumonia","year":2024,"lang":"en","type":"article","venue":"Hospital Pediatrics","topic":"Antibiotic Use and Resistance","field":"Immunology and Microbiology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"U.S. National Library of Medicine","keywords":"Guideline; Medicine; Concordance; Community-acquired pneumonia; Algorithm; Cohort; Retrospective cohort study; Clinical decision support system; Electronic health record; Pneumonia; Cohort study; MEDLINE; Internal medicine; Data mining; Decision support system; Health care; Pathology; Computer science","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":[0.01002352,0.001133815,0.001341726,0.005224817,0.0008293588,0.002194425,0.001817718,0.001610383,0.001454075],"category_scores_gemma":[0.04993973,0.0004763022,0.00111593,0.002197465,0.0004740176,0.001256101,0.001116784,0.001117118,0.0005583442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001525587,"about_ca_system_score_gemma":0.003894273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01024998,"about_ca_topic_score_gemma":0.01003602,"domain_scores_codex":[0.9922012,0.003328797,0.001236496,0.001723898,0.001279945,0.0002296367],"domain_scores_gemma":[0.9662372,0.02040561,0.003589929,0.0009745576,0.008425822,0.0003667777],"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":[0.0008418304,0.0007438198,0.3533597,0.000465695,0.001183397,0.000330477,0.000540446,0.0894886,0.002599074,0.001568645,0.01298392,0.5358945],"study_design_scores_gemma":[0.0002462513,0.0002635901,0.04088779,0.0002294041,0.0002693692,0.0006244538,0.0001908456,0.9469935,0.003065099,0.003118493,0.004046191,0.00006506214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2697433,0.001574448,0.7103781,0.001711293,0.0002805149,0.002301571,0.003391479,0.00581814,0.004801137],"genre_scores_gemma":[0.459359,0.0001985371,0.5358791,0.000418848,0.00007920396,0.0008307066,0.002474537,0.00009275075,0.0006674014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01024998,"threshold_uncertainty_score":0.05301005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479543607079273,"score_gpt":0.2941068185615858,"score_spread":0.2793113824907931,"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."}}