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Record W4324116984 · doi:10.1016/j.gimo.2023.100385

P357: Replication of genetic variation associated with COVID-19 clinical outcomes: The GENCOV Prospective Cohort Study

2023· article· en· W4324116984 on OpenAlexaff
Dawit Wolday, Erika Frangione, Chun Yiu Jordan Fung, Gregory Morgan, Selina Casalino, Sunakshi Chowdhary, Navneet Aujla, Chloe Mighton, David Di Iorio, Elisa Lapadula, Juliet Young, Mackenzie Scott, Abdul Noor, Yvonne Bombard, Saranya Arnoldo, Erin Bearss, Alexandra Binnie, David J. Richardson, Deepanjali Kaushik, Bjug Borgundvaag, Howard Chertkow, Marc Clausen, Marc Dagher, Luke Devine, Hanna Faghfoury, Steven Friedman, Anne‐Claude Gingras, Zeeshan Khan, Iris L. K. Wong, Natasha Zarei, Lee Goneau, Seth Stern, Ahmed Taher, Tony Mazzulli, Allison McGeer, Shelley McLeod, Jared T. Simpson, Trevor J. Pugh, Lisa J. Strug, Elika Garg, L. F. Elliott, Jordan Lerner‐Ellis

Bibliographic record

VenueGenetics in Medicine Open · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsSimon Fraser UniversityHospital for Sick ChildrenWomen's College HospitalBaycrest HospitalWilliam Osler Health SystemUniversity of TorontoUniversity Health NetworkLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalOntario Institute for Cancer ResearchToronto Public Health
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Replication (statistics)Prospective cohort studyCohort2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Genetic variationCohort studyMedicineVirologyBiologyGeneticsInternal medicineGeneDiseaseOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Cumulative evidence suggest that host genetic variability plays a pivotal role in SARS-CoV-2 (COVID-19) clinical outcomes. Here, we aimed to evaluate the impact of known single-nucleotide polymorphisms (SNPs) as risk factors for hospitalization as a result of COVID-19 infection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.453
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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