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Record W3162558207 · doi:10.1371/journal.pone.0245031

The Biobanque québécoise de la COVID-19 (BQC19)—A cohort to prospectively study the clinical and biological determinants of COVID-19 clinical trajectories

2021· article· en· W3162558207 on OpenAlexafffundabout
Simon Rousseau, Ma’n H. Zawati, Daniel Auld, Michaël Chassé, Daniel Coderre, Emilia Liana Falcone, Nicolas Gauthier, Nathalie Grandvaux, François Gros‐Louis, Carole Jabet, Yann Joly, Daniel E. Kaufmann, Catherine Laprise, Catherine Larochelle, François Maltais, Anne‐Marie Mes‐Masson, Alexandre Montpetit, Alain Piché, J. Brent Richards, Sze Man Tse, Alexis F. Turgeon, Gustavo Turecki, Donald C. Vinh, Han Ting Wang, Vincent Mooser

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsHôpital Maisonneuve-RosemontDouglas Mental Health University InstituteCentre Hospitalier Universitaire Sainte-JustineJewish General HospitalSante MontrealCentre hospitalier universitaire de QuébecCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier Universitaire de SherbrookeMontreal Clinical Research InstituteUniversité du Québec à ChicoutimiUniversité de MontréalFonds de Recherche du Québec - SantéUniversité LavalCentre Hospitalier de l’Université de MontréalGenome CanadaCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanMcGill University Health CentreMcGill UniversityHôpital du Sacré-Cœur de MontréalMcGill Genome CentreUniversité de Sherbrooke
FundersInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalJewish General HospitalMcGill University Health CentrePublic Health Agency of CanadaMcGill UniversityGénome QuébecFonds de Recherche du Québec - SantéUniversité du Québec à Chicoutimi
KeywordsBiobankPandemicOutbreakCoronavirus disease 2019 (COVID-19)DiseaseMedicineVirologyBiologyBioinformaticsInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

SARS-CoV-2 infection causing the novel coronavirus disease 2019 (COVID-19) has been responsible for more than 2.8 million deaths and nearly 125 million infections worldwide as of March 2021. In March 2020, the World Health Organization determined that the COVID-19 outbreak is a global pandemic. The urgency and magnitude of this pandemic demanded immediate action and coordination between local, regional, national, and international actors. In that mission, researchers require access to high-quality biological materials and data from SARS-CoV-2 infected and uninfected patients, covering the spectrum of disease manifestations. The "Biobanque québécoise de la COVID-19" (BQC19) is a pan-provincial initiative undertaken in Québec, Canada to enable the collection, storage and sharing of samples and data related to the COVID-19 crisis. As a disease-oriented biobank based on high-quality biosamples and clinical data of hospitalized and non-hospitalized SARS-CoV-2 PCR positive and negative individuals. The BQC19 follows a legal and ethical management framework approved by local health authorities. The biosamples include plasma, serum, peripheral blood mononuclear cells and DNA and RNA isolated from whole blood. In addition to the clinical variables, BQC19 will provide in-depth analytical data derived from the biosamples including whole genome and transcriptome sequencing, proteome and metabolome analyses, multiplex measurements of key circulating markers as well as anti-SARS-CoV-2 antibody responses. BQC19 will provide the scientific and medical communities access to data and samples to better understand, manage and ultimately limit, the impact of COVID-19. In this paper we present BQC19, describe the process according to which it is governed and organized, and address opportunities for future research collaborations. BQC19 aims to be a part of a global communal effort addressing the challenges of COVID-19.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.244
Threshold uncertainty score0.491

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.245
GPT teacher head0.443
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations71
Published2021
Admission routes3
Has abstractyes

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