{"id":"W3184445017","doi":"10.3390/diagnostics11081309","title":"Forecasting COVID-19 Severity by Intelligent Optical Fingerprinting of Blood Samples","year":2021,"lang":"en","type":"article","venue":"Diagnostics","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020; Fundação para a Ciência e a Tecnologia; Engineering and Physical Sciences Research Council; Centre hospitalier universitaire Sainte-Justine","keywords":"Triage; Coronavirus disease 2019 (COVID-19); Fingerprint (computing); Medicine; Intensive care; Intensive care unit; Disease; Computer science; Emergency medicine; Artificial intelligence; Intensive care medicine; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0007549641,0.0001611298,0.0005116091,0.00005697605,0.0001155086,0.00002839826,0.0001326282,0.0001096005,0.0001806378],"category_scores_gemma":[0.5728069,0.0001509305,0.0001703507,0.0003197281,0.0002857173,0.00004037929,0.0005528994,0.0004176761,0.00000982347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001001393,"about_ca_system_score_gemma":0.0007752474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001332315,"about_ca_topic_score_gemma":0.00002564097,"domain_scores_codex":[0.9979505,0.00008295922,0.0005913987,0.0003914925,0.0005564272,0.0004272059],"domain_scores_gemma":[0.9383881,0.05970892,0.0001543881,0.0005039159,0.0005860015,0.0006586352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001889944,0.003123036,0.9194397,0.005184251,0.0009121413,0.002018502,0.001197486,0.0004411443,0.006549539,0.002878207,0.01110489,0.04696211],"study_design_scores_gemma":[0.009670958,0.002908364,0.09884857,0.003762993,0.003587442,0.0001731852,0.003070634,0.01845431,0.4861418,0.007775299,0.3637464,0.001860095],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9225278,0.002969297,0.05590713,0.01634959,0.0002749301,0.0006588076,0.0002326929,0.0001298196,0.000949962],"genre_scores_gemma":[0.9839894,0.003472583,0.008813832,0.003244201,0.0001634761,0.00002324751,0.00006150235,0.00002368613,0.0002080699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8205911,"threshold_uncertainty_score":0.6154765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1278443480046252,"score_gpt":0.4232187182999953,"score_spread":0.2953743702953701,"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."}}