{"id":"W4407706069","doi":"10.3390/cancers17040687","title":"Post-COVID-19 Condition Prediction in Hospitalised Cancer Patients: A Machine Learning-Based Approach","year":2025,"lang":"en","type":"article","venue":"Cancers","topic":"COVID-19 and healthcare impacts","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Russian Science Foundation; Barts Charity","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Cancer; Computer science; Machine learning; Artificial intelligence; Medicine; Virology; Internal medicine; Infectious disease (medical specialty)","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":[0.001692788,0.0004317354,0.0006782144,0.002552954,0.0003145405,0.001421272,0.0005627064,0.0006305333,0.00124523],"category_scores_gemma":[0.00495775,0.0001558182,0.0008602106,0.001228304,0.0002004428,0.0005412581,0.000694508,0.001008617,0.0002900679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007768028,"about_ca_system_score_gemma":0.0008403399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006332113,"about_ca_topic_score_gemma":0.007020696,"domain_scores_codex":[0.9993911,0.0002419457,0.00006820884,0.000146608,0.00006986321,0.00008231387],"domain_scores_gemma":[0.9975594,0.001294978,0.0005532583,0.00008549094,0.0003119319,0.0001949704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002673636,0.0002562574,0.952928,0.00009060033,0.0001918895,0.000125356,0.00008861248,0.01314731,0.0002474207,0.0001763295,0.0009394079,0.0315414],"study_design_scores_gemma":[0.00003523569,0.0005668603,0.6364341,0.0002005971,0.0002328159,0.0003766791,0.0008060967,0.3573759,0.0005708977,0.002099383,0.001256921,0.00004439146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779236,0.0017345,0.01463602,0.001129054,0.00007459342,0.0001276789,0.002574912,0.0001039873,0.001695676],"genre_scores_gemma":[0.9937499,0.0003028464,0.004234226,0.00004927381,0.0000414751,0.00003692825,0.001383032,0.000004546653,0.0001977763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006332113,"threshold_uncertainty_score":0.01259047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02194637761862477,"score_gpt":0.3530024768755735,"score_spread":0.3310560992569487,"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."}}