{"id":"W3108430663","doi":"10.1093/jalm/jfaa231","title":"COVID-19 Pandemic Planning: Simulation Models to Predict Biochemistry Test Capacity for Patient Surges","year":2020,"lang":"en","type":"article","venue":"The Journal of Applied Laboratory Medicine","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Roche (Canada); Saskatchewan Health; Saskatchewan Health Authority","funders":"","keywords":"Workload; Turnaround time; Pandemic; Coronavirus disease 2019 (COVID-19); Test (biology); Vendor; Throughput; Medical emergency; Medicine; Instrumentation (computer programming); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Emergency medicine; Operations management; Simulation; Engineering; Business; Telecommunications","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":[0.001089993,0.0001805582,0.0004388444,0.0001018829,0.000115272,0.000006862017,0.0001334366,0.00009641104,0.00002354682],"category_scores_gemma":[0.004365793,0.0001147407,0.00005222057,0.0004814997,0.0001133328,0.00005078585,0.00002503793,0.0003887963,0.000001803839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001843651,"about_ca_system_score_gemma":0.0004656624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005745673,"about_ca_topic_score_gemma":6.584286e-7,"domain_scores_codex":[0.9984754,0.00003756045,0.000645369,0.0001606577,0.0004818974,0.0001990635],"domain_scores_gemma":[0.9968504,0.001493145,0.0004899029,0.0001799042,0.0004620616,0.0005245528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002649379,0.0000736795,0.001891192,0.0002733609,0.0001035487,0.00002026335,0.007775452,0.04176566,0.9363241,0.00001818863,0.007915308,0.001189943],"study_design_scores_gemma":[0.01548274,0.006653142,0.0004120238,0.0009383303,0.001423082,0.0003931814,0.01202923,0.09435512,0.7596162,0.0009775482,0.1070721,0.0006473211],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700927,0.0003094361,0.02497083,0.003441508,0.0001443534,0.0005551405,0.00003106748,0.00008624739,0.0003687452],"genre_scores_gemma":[0.9714376,0.000007216936,0.000708992,0.02684348,0.0009586725,0.00001057859,0.000004221264,0.00002665963,0.000002576717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1767079,"threshold_uncertainty_score":0.5226578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1278583056547848,"score_gpt":0.335207048654298,"score_spread":0.2073487429995132,"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."}}