{"id":"W4391655713","doi":"10.1183/13993003.02301-2023","title":"Cluster analysis to identify long COVID phenotypes using<sup>129</sup>Xe magnetic resonance imaging: a multicentre evaluation","year":2024,"lang":"en","type":"article","venue":"European Respiratory Journal","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; University of British Columbia","funders":"National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; Duke Health; St. Paul's Foundation","keywords":"Medicine; Coronavirus disease 2019 (COVID-19); Magnetic resonance imaging; Cluster (spacecraft); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Phenotype; Nuclear magnetic resonance; Nuclear medicine; Virology; Radiology; Pathology; Physics; Genetics; Outbreak","routes":{"ca_aff":true,"ca_fund":true,"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":[0.011895,0.001231853,0.001227638,0.002182169,0.001021184,0.001319773,0.001445165,0.0007620252,0.001316812],"category_scores_gemma":[0.01732794,0.0004135439,0.001609503,0.001805367,0.0008560642,0.0005925625,0.002586292,0.000548103,0.00034015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008639766,"about_ca_system_score_gemma":0.001110525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004742815,"about_ca_topic_score_gemma":0.005480967,"domain_scores_codex":[0.9943873,0.002902881,0.0004934039,0.001006378,0.0008628817,0.0003471419],"domain_scores_gemma":[0.9886507,0.003340048,0.00200284,0.001804141,0.003002837,0.001199478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01002835,0.0008381772,0.8912215,0.000592562,0.003022911,0.0006585899,0.003797797,0.007495663,0.01851726,0.0004673751,0.003137138,0.06022265],"study_design_scores_gemma":[0.0003917027,0.003895265,0.9632561,0.00006249634,0.0006201684,0.000991724,0.001944503,0.02417048,0.002554461,0.0005237014,0.001477228,0.0001121293],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869338,0.0001990194,0.01094259,0.000109591,0.00002524807,0.0005416629,0.0008027468,0.000122335,0.0003229637],"genre_scores_gemma":[0.9829925,0.00006923137,0.01472333,0.00002737768,0.00001593452,0.0004185218,0.001385814,0.00007432933,0.0002930358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.011895,"threshold_uncertainty_score":0.06290752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04229745982990647,"score_gpt":0.3596498125237885,"score_spread":0.3173523526938821,"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."}}