Longitudinal Study on Seroprevalence and Immune Response to SARS-CoV-2 in a Population of Food and Retail Workers Through Transformation of ELISA Datasets
Bibliographic record
Abstract
Abstract Since the onset of the global pandemic caused by the emergence and spread of SARS-CoV-2 in early 2020, numerous studies have been conducted worldwide to understand our immune response to the virus. This study investigates the humoral response elicited by vaccination and by SARS-CoV-2 infection in the poorly studied food and retail workers in the Québec City area. The 1.5-year study period spans from early 2021, when vaccination became available in this region, to mid-2022, following waves of virulence due to the emergence of the first Omicron variants. Cross-correlated with data on workplace protective measures, pre-existing conditions, activities and other potentially relevant factors, this longitudinal study applies recently developed ELISA data transformation to our dataset to obtain normal distribution. This unlocked the possibility to use the ANOVA-Welsh method for statistical analysis to obtain a statistical perspective of the serological response. Our work allows the identification of factors contributing to statistically relevant differences in the humoral response of the cohort and strengthens the utility of the use of decentralized approaches to serological analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".