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Record W4392588478 · doi:10.1016/j.gimo.2024.101614

P710: Phenome-wide association study (PheWAS) for the Canadian HostSeq Biobank

2024· article· en· W4392588478 on OpenAlexaffabout
Erika Frangione, Xinyi Xu, Vincent Chapdelaine, Selina Casalino, Navneet Aujla, Radhika Mahajan, Lochana Jayachandran, Gregory Morgan, Mackenzie Scott, Juliet Young, Brendan C. Dickson, Saranya Arnoldo, Erin Bearss, Alexandra Binnie, Bjug Borgundvaag, Howard Chertkow, Marc Clausen, Marc Dagher, Luke Devine, Steven Friedman, Anne‐Claude Gingras, Lee Goneau, Deepanjali Kaushik, Zeeshan Khan, Elisa Lapadula, Georgia MacDonald, Tony Mazzulli, Allison McGeer, Shelley McLeod, Chloe Mighton, Trevor J. Pugh, David J. Richardson, Jared T. Simpson, Seth Stern, Lisa J. Strug, Ahmed Taher, Iris L. K. Wong, Natasha Zarei, Elena Greenfeld, Yvonne Bombard, Abdul Noor, Hanna Faghfoury, Jennifer Taher, Daniel Taliun, Jordan Lerner‐Ellis

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsHospital for Sick ChildrenOntario Institute for Cancer ResearchWomen's College HospitalUniversity Health NetworkUniversity of TorontoMcGill UniversityBaycrest HospitalWilliam Osler Health SystemLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsPhenomeBiobankAssociation (psychology)BiorepositoryBiologyComputational biologyMedicineBioinformaticsGeneticsPsychologyGenomeGene

Abstract

fetched live from OpenAlex

The HostSeq database is a repository containing genome sequencing (GS) results and harmonized clinical data for ∼10,000 Canadians infected with SARS-CoV-2 over the COVID-19 pandemic. HostSeq is being used to facilitate research efforts in understanding the risks for disease and health outcomes. Phenome-wide association studies (PheWAS) are an approach used to identify associations between a large number of clinical phenotypes simultaneously and specific genetic markers. This comprehensive analysis can potentially reveal novel genetic risk factors related to diverse health conditions and inform disease prevention and treatment strategies.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.378
Teacher spread0.340 · 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 designNot applicable
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
Published2024
Admission routes2
Has abstractyes

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