{"id":"W4282026501","doi":"10.1101/2022.05.27.22275708","title":"Identifying COVID-19 phenotypes using cluster analysis and assessing their clinical outcomes","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Centre Intégré de Santé et de Services Sociaux des Laurentides; Université Laval; Cegep regional de Lanaudiere; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec; Réseau en Bio-Imagerie du Quebec","keywords":"Phenotype; Hierarchical clustering; Cluster (spacecraft); Medicine; Hypoxemia; Comorbidity; Cluster analysis; Internal medicine; Biology; Computer science; Machine learning; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002073573,0.0006570601,0.0005892636,0.002824322,0.001076227,0.001328712,0.0007032912,0.0003786832,0.001254932],"category_scores_gemma":[0.006022951,0.0001874025,0.0008047578,0.002286594,0.0005396313,0.0003693239,0.001101802,0.0006677464,0.0002581491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002137371,"about_ca_system_score_gemma":0.002040692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06554785,"about_ca_topic_score_gemma":0.06086344,"domain_scores_codex":[0.9989185,0.0002222076,0.0001222307,0.0002209307,0.0002797416,0.0002364646],"domain_scores_gemma":[0.9964055,0.0006019954,0.001001821,0.000211001,0.001359218,0.0004205125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001768936,0.00002751474,0.9923931,0.00002746176,0.00006767971,0.00008976183,0.0001275696,0.00113497,0.0009080463,0.0001013411,0.0007646484,0.004180982],"study_design_scores_gemma":[0.00001865861,0.00011343,0.9794088,0.00003686626,0.00004241828,0.0002614657,0.0008734837,0.01732978,0.0008572305,0.0003360265,0.000698751,0.00002314107],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890767,0.0001940798,0.005944947,0.0002051618,0.00002387893,0.000192314,0.003332925,0.00006710795,0.0009628615],"genre_scores_gemma":[0.9903452,0.00006921771,0.005814051,0.00003114821,0.000008480565,0.00007667366,0.003456715,0.00001321814,0.0001852125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06554785,"threshold_uncertainty_score":0.1303326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.281648051814426,"score_gpt":0.5534583863062058,"score_spread":0.2718103344917798,"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."}}