{"id":"W4401696597","doi":"10.12688/wellcomeopenres.20278.2","title":"VIVALDI Cohort Profile: Using linked, routinely collected data and longitudinal blood sampling to characterise COVID-19 infections, vaccinations, and related outcomes in care home staff and residents in England","year":2024,"lang":"en","type":"preprint","venue":"Wellcome Open Research","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"Health Data Research UK; University College London; National Institute for Health and Care Research; University College London Hospitals Biomedical Research Centre; Wellcome; National Institute for Social Care and Health Research; Public Health England; Department of Health and Social Care; Wellcome Trust","keywords":"Medicine; Coronavirus disease 2019 (COVID-19); Cohort; Longitudinal study; Sampling (signal processing); Blood sampling; 2019-20 coronavirus outbreak; Cohort study; Vaccination; Longitudinal data; Family medicine; Gerontology; Outbreak; Demography; Virology; Internal medicine; Infectious disease (medical specialty); Disease; Pathology","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":[0.004068578,0.0004059178,0.0004828276,0.001287388,0.0005489756,0.001283968,0.0009406866,0.0005753996,0.001847184],"category_scores_gemma":[0.01054523,0.0005301847,0.0005209258,0.001566382,0.0002643019,0.0006117267,0.002011414,0.0006263013,0.0006795669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022211,"about_ca_system_score_gemma":0.001147428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06987327,"about_ca_topic_score_gemma":0.07393011,"domain_scores_codex":[0.9976971,0.0007836836,0.0002338127,0.0007370766,0.0003003392,0.0002480516],"domain_scores_gemma":[0.9939927,0.000808044,0.001639321,0.002062872,0.0009294867,0.0005674685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007996926,0.00007959905,0.9882392,0.0001091485,0.0002567476,0.0001499344,0.0008805327,0.0001563216,0.0006959508,0.0002294827,0.002555334,0.005848136],"study_design_scores_gemma":[0.0001100919,0.0002393692,0.9928542,0.00008681459,0.0001146843,0.0003548599,0.0007651181,0.0004127711,0.0001700525,0.0001113159,0.004755391,0.00002540016],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676293,0.0006049906,0.002067473,0.0001783399,0.00003676778,0.0005037083,0.0272555,0.00004581912,0.001677993],"genre_scores_gemma":[0.940759,0.0005301088,0.006218538,0.0003120904,0.00005930513,0.001249587,0.04858332,0.00005309603,0.002234816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06987327,"threshold_uncertainty_score":0.1389331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.244074335998255,"score_gpt":0.5203918648620001,"score_spread":0.2763175288637451,"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."}}