Biobanque québécoise de la COVID-19 (BQC19), a COVID-19 biobank to support Canadian health research
Bibliographic record
Abstract
Biobanking plays a crucial role in promptly generating effective solutions to combat emerging infectious diseases. In direct response to the COVID-19 crisis, Quebec established the “Biobanque québécoise de la COVID-19” (BQC19) in March 2020. BQC19 swiftly amassed comprehensive demographic and clinical data and biosamples, from individuals across the province seeking care at participating hospitals and outpatient clinics. This article underscores the transformative potential of BQC19, which has thousands of digitized demographics and clinical records, biosamples from various stages of the pandemic, and provides digital access to the results of numerous multi-omic analyses. By advanced handling and analytical approaches to large sets of genomic, transcriptomic, metabolomic and proteomic data, BQC19 provides a platform to investigate diagnostic and prognostic biomarkers and explore patient stratification for a more personalized approach. Through BQC19, we examine the pivotal role of biobanks in deciphering the complexities of COVID-19 and their capacity to enhance readiness for future infectious threats. BQC19’s dynamic framework, encompassing participating hospitals, clinics, research institutions, core analysis laboratory data, and a network of Canadian and international medical professionals and researchers, exemplifies its versatility. With standardized protocols, secure data management, ethical guidelines and community engagement, BQC19 stands as a beacon for proactive research on emerging diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".