{"id":"W2009997847","doi":"10.1111/j.1365-2044.2006.04803.x","title":"Automated calculation of ‘early warning scores’","year":2006,"lang":"en","type":"letter","venue":"Anaesthesia","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Vital signs; Medicine; Early warning score; Raw data; Set (abstract data type); Weighting; Documentation; Warning system; Data collection; Medical emergency; Data mining; Computer science; Statistics; Surgery","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":[],"consensus_categories":[],"category_scores_codex":[0.00006555571,0.0002414607,0.0005387704,0.000212934,0.0000345943,0.00001417367,0.00006964667,0.0004830212,0.00006246957],"category_scores_gemma":[0.00001530611,0.0001967448,0.000227383,0.0001685552,0.00004688602,0.00003355974,0.0000115933,0.0003723716,0.0001036177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001011215,"about_ca_system_score_gemma":0.00008803061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001236611,"about_ca_topic_score_gemma":0.000008144391,"domain_scores_codex":[0.9986899,0.00005025277,0.0003521911,0.0002869061,0.0003822912,0.0002384919],"domain_scores_gemma":[0.9991888,0.00004556025,0.0002411918,0.000384615,0.0001014413,0.00003837195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00000814036,0.00008449228,0.1980074,0.00009863274,0.0001277831,0.0008969934,0.00004343866,0.000009407378,0.00001256943,0.00001216776,0.7980501,0.002648859],"study_design_scores_gemma":[0.0008094392,0.0006060391,0.8077285,0.0006519069,0.0006268894,0.00002772871,0.000002006296,0.000404172,0.0003687824,0.00003157802,0.1885224,0.0002205722],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8166813,0.0008953797,0.00001069461,0.1796095,0.00009125484,0.0005405621,0.000009570434,0.0003751755,0.001786507],"genre_scores_gemma":[0.8250515,0.00006804687,0.0007165382,0.1701018,0.0008258803,0.00006628899,0.001602781,0.0001112666,0.001455877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6097211,"threshold_uncertainty_score":0.8023022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04758508727667624,"score_gpt":0.3123593089156596,"score_spread":0.2647742216389833,"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."}}