Cohort profile: A Québec-based plasma donor biobank to study COVID-19 immunity (PlasCoV)
Bibliographic record
Abstract
PURPOSE: The long-term humoral immunity to COVID-19 is not well understood owing to the continuous emergence of new variants of concern, the evolving vaccine-induced and infection-induced immunity, and the limited duration of follow-up in previous studies. As the sole blood service in Québec (Canada), Héma-Québec established a COVID-19-focused biobank ('PlasCoV') in April 2021. PARTICIPANTS: As of January 2022, the biobank included 86 483 plasma samples from 15 502 regular donors (age range=18-84 years, females=49.7%), for an average of 5.6 donations per donor. Nearly two-thirds (65.6%) of biobank donors made at least two donations, with many donors having provided samples prevaccination and postvaccination (3061 (19.7%)) or preinfection and postinfection (131 (0.8%)), thus allowing for longitudinal studies on vaccine-induced and infection-induced immunity. FINDINGS TO DATE: A study that used PlasCoV samples revealed that previously infected individuals who received a single dose of the BNT162b2 COVID-19 vaccine exhibited the strongest immune response. By contrast, SARS-CoV-2-naïve individuals required two vaccine doses to produce a maximal immune response. Furthermore, the results of a four-phase seroprevalence study indicated that the antinucleocapsid (N) response wanes rapidly, so that up to one-third of previously infected donors were seronegative for anti-N. FUTURE PLANS: Donations from individuals who consented to participate before 1 October 2022 will be collected up until 31 March 2023. This plasma biobank will facilitate the conduct of longitudinal studies on COVID-19 immunity, thus helping to provide valuable insights into the anti-SARS-CoV-2 immune response and its persistence, and the effects of vaccination and variants on the specificity of the anti-SARS-CoV-2 immune response.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
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".