Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, caused by the SARS-CoV-2 virus, has rapidly evolved since late 2019, due to highly transmissible Omicron variants. While most Canadian paramedics have received COVID-19 vaccination, the optimal ongoing vaccination strategy is unclear. We investigated neutralizing antibody (NtAb) response against wild-type (WT) Wuhan Hu-1 and Omicron BA.4/5 lineages based on the number of doses and past SARS-CoV-2 infection, at 18 months post initial vaccination (with a Wuhan Hu-1 platform mRNA vaccine [BNT162b2 or mRNA-1273}). Demographic information, previous COVID-19 vaccination, infection history, and blood samples were collected from paramedics 18 months post initial mRNA COVID-19 vaccine dose. Outcome measures were ACE2 percent inhibition against Omicron BA.4/5 and WT antigens. We compared outcomes based on number of vaccine doses (two vs. three) and previous SARS-CoV-2 infection status, using the Mann-Whitney U test. Of 657 participants, the median age was 40 years (IQR 33-50) and 251 (42%) were females. Overall, median percent inhibition to BA.4/5 and WT was 71.61% (IQR 39.44-92.82) and 98.60% (IQR 83.07-99.73), respectively. Those with a past SARS-CoV-2 infection had a higher median percent inhibition to BA.4/5 and WT, when compared to uninfected individuals overall and when stratified by two or three vaccine doses. When comparing two vs. three WT vaccine doses among SARS-CoV-2 negative participants, we did not detect a difference in BA.4/5 percent inhibition, but there was a difference in WT percent inhibition. Among those with previous SARS-CoV-2 infection(s), when comparing two vs. three WT vaccine doses, there was no observed difference between groups. These findings demonstrate that additional Wuhan Hu-1 platform mRNA vaccines did not improve NtAb response to BA.4/5, but prior SARS-CoV-2 infection enhances NtAb 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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.828 | 0.641 |
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".