{"id":"W1985921357","doi":"10.1186/cc14025","title":"Assessing the value of a real-time electronic screening algorithm for early detection of severe sepsis in the emergency department","year":2014,"lang":"en","type":"article","venue":"Critical Care","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Children's Health Foundation; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Financiadora de Estudos e Projetos; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Japan Society for the Promotion of Science; Russian Foundation for Basic Research; London Health Sciences Centre","keywords":"Medicine; Sepsis; Septic shock; Emergency department; Severe sepsis; Electronic surveillance; Emergency medicine; Algorithm; Predictive value; Electronic medical record; Intensive care medicine; Medical emergency; Internal medicine; Computer security; Computer science; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0126722,0.0006030759,0.0005892616,0.001781944,0.0003063611,0.00160523,0.001068108,0.001514765,0.001572393],"category_scores_gemma":[0.07508999,0.0002450728,0.000494897,0.00112097,0.000519782,0.001559297,0.0008638078,0.0006594924,0.0005405335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000682089,"about_ca_system_score_gemma":0.0009057269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006450434,"about_ca_topic_score_gemma":0.0006166615,"domain_scores_codex":[0.9835369,0.01240039,0.00109205,0.0006406184,0.002048761,0.0002813319],"domain_scores_gemma":[0.9170941,0.06314703,0.008676591,0.003274636,0.006698823,0.001108818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01835209,0.004951491,0.6701506,0.0006666353,0.0006078141,0.00016579,0.0003230757,0.009584378,0.003088492,0.001253941,0.001906928,0.2889487],"study_design_scores_gemma":[0.002479243,0.05358268,0.6229593,0.0006093521,0.001455004,0.001471751,0.0007361497,0.290674,0.01708162,0.002880783,0.005813031,0.0002571809],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801743,0.001436047,0.01238218,0.0009603233,0.0001550251,0.0005712283,0.0007298158,0.0002367464,0.003354248],"genre_scores_gemma":[0.972097,0.0002746299,0.02602952,0.0004347073,0.00009296091,0.0002790393,0.0004040188,0.00001597674,0.00037218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0126722,"threshold_uncertainty_score":0.06701779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04531892014351987,"score_gpt":0.3781554411975633,"score_spread":0.3328365210540435,"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."}}