{"id":"W4407483909","doi":"10.1101/2025.02.12.25322164","title":"Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Long-Term Effects of COVID-19","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prevention of Organ Failure; Systems, Applications & Products in Data Processing (Canada)","funders":"Genentech; Shenzhen Center for Health Information; U.S. Department of Defense; Gilead Sciences; Regeneron Pharmaceuticals; Teva Pharmaceutical Industries; National Institutes of Health; Yale University; Pfizer","keywords":"Coronavirus disease 2019 (COVID-19); Medicine; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Psychological intervention; Intensive care medicine; Disease; Predictive modelling; Internal medicine; Machine learning; Infectious disease (medical specialty); Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003294683,0.001086554,0.0008086421,0.001015557,0.0002548985,0.00115226,0.000469669,0.0007454182,0.002106739],"category_scores_gemma":[0.009297627,0.0001963902,0.000908888,0.0004437771,0.0002934053,0.0006609213,0.0005948713,0.001479456,0.0007087698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006629818,"about_ca_system_score_gemma":0.0008211652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004147075,"about_ca_topic_score_gemma":0.003208456,"domain_scores_codex":[0.9993871,0.0002745212,0.0000416767,0.0001314582,0.00005156398,0.0001135975],"domain_scores_gemma":[0.9948556,0.003895453,0.0004914379,0.000184149,0.000351211,0.0002220628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001184714,0.0008504235,0.3403268,0.0001366771,0.0005885012,0.0003046702,0.0001125119,0.5448977,0.001812386,0.001430959,0.006204553,0.1021501],"study_design_scores_gemma":[0.00002391044,0.0002685042,0.01865873,0.00003384138,0.00007286576,0.00006722914,0.00003398498,0.9772739,0.0005645628,0.002348271,0.0006370085,0.00001718852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9190882,0.002516477,0.06618529,0.003951916,0.0002939977,0.0001215807,0.003611623,0.0008301435,0.003400847],"genre_scores_gemma":[0.9883032,0.0004260872,0.007079951,0.0002978003,0.0001320696,0.00006766155,0.002359681,0.00002827679,0.00130512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004147075,"threshold_uncertainty_score":0.01742411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0579030430462996,"score_gpt":0.3589659957882013,"score_spread":0.3010629527419017,"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."}}