{"id":"W2143932921","doi":"10.1001/dmp.2011.45","title":"Information Technology Systems for Critical Care Triage and Medical Response During an Influenza Pandemic: A Review of Current Systems","year":2011,"lang":"en","type":"review","venue":"Disaster Medicine and Public Health Preparedness","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research; U.S. Department of Homeland Security","keywords":"Triage; Pandemic; Medical emergency; Preparedness; Medicine; Political science; Business; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Disease","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.009474971,0.00100657,0.001331967,0.02126931,0.001113473,0.003660415,0.001803185,0.002072214,0.002337092],"category_scores_gemma":[0.02325777,0.000714122,0.001541442,0.02497582,0.002011025,0.00507605,0.001230728,0.001723803,0.0005885075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01023694,"about_ca_system_score_gemma":0.02517226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05485404,"about_ca_topic_score_gemma":0.0894782,"domain_scores_codex":[0.9939739,0.0015811,0.001777504,0.0003947158,0.001991583,0.0002812395],"domain_scores_gemma":[0.9610364,0.02720798,0.003447064,0.0005019858,0.007245874,0.0005607067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006823383,0.00006025843,0.002150997,0.1479352,0.000326893,0.0001451188,0.001373216,0.0006266091,0.0003991404,0.005971881,0.02727693,0.8136655],"study_design_scores_gemma":[0.00003492807,0.0002215313,0.01773205,0.2839727,0.001526035,0.0006776582,0.00214806,0.0005648116,0.0005548752,0.002973536,0.6895115,0.00008227836],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002197556,0.9969171,0.0001932203,0.001197386,0.0001373178,0.00002184312,0.00004693316,0.00001014033,0.001256212],"genre_scores_gemma":[0.002513396,0.9961892,0.0006282547,0.0003880446,0.00009728524,0.00002582305,0.00006536495,0.000002835452,0.00008990039],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.05485404,"threshold_uncertainty_score":0.1090695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1924341711089566,"score_gpt":0.483518225289968,"score_spread":0.2910840541810114,"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."}}