{"id":"W3212615285","doi":"10.1101/2021.11.08.21266091","title":"Data-driven prognosis for COVID-19 patients based on symptoms and age","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Code (set theory); Computer science; Diagnosis code; Medicine; Integer (computer science); Health records; Medical emergency; Health care; Internal medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000608569,0.0004817902,0.0009001275,0.0003202341,0.0001380414,0.0001570084,0.0005499037,0.0004070564,0.0001076889],"category_scores_gemma":[0.005516545,0.0004553148,0.0002005283,0.0001987799,0.0001468004,0.00006151962,0.001161435,0.0005807297,0.000009610901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004165011,"about_ca_system_score_gemma":0.001195879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001195049,"about_ca_topic_score_gemma":0.00008434034,"domain_scores_codex":[0.9965442,0.000166872,0.0005223985,0.001690061,0.0006528092,0.0004235968],"domain_scores_gemma":[0.9953197,0.001359572,0.0002883689,0.002217309,0.0002355663,0.0005794677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004161313,0.002163598,0.9234401,0.007836089,0.0003995065,0.0003702176,0.0005009468,0.001249169,0.0001092221,0.00001428327,0.05727767,0.006223054],"study_design_scores_gemma":[0.0125859,0.001598263,0.4718808,0.005364333,0.0024562,0.000007839673,0.00005678723,0.07985165,0.0007511972,0.0001279175,0.4238133,0.001505842],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9479165,0.0002550004,0.002598921,0.04118644,0.001112324,0.004531784,0.001979661,0.0003451438,0.00007421032],"genre_scores_gemma":[0.9127672,0.0001058695,0.005473279,0.06876735,0.0003820525,0.001150626,0.01106368,0.0001558963,0.0001339959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4515593,"threshold_uncertainty_score":0.9997898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08636345986627644,"score_gpt":0.3644409440837335,"score_spread":0.278077484217457,"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."}}