Using blood donors and solid organ transplant donors and recipients to estimate the seroprevalence of cytomegalovirus and Epstein–Barr virus in Canada: A cross-sectional study
Bibliographic record
Abstract
Background: Cytomegalovirus (CMV) and Epstein-Barr virus (EBV) infections are common, causing significant morbidity in pregnancy (congenital CMV) and transplant recipients (CMV, EBV). Canadian prevalence data are needed to model disease burden and develop strategies for future vaccines. We estimated prevalence using screening data from blood donors and solid organ transplant (SOT) donors and recipients. Methods: = 1,253,350) (2005-2014), studying associations with age, sex, organ, year, and geographic region. Results: CMV seroprevalence rises gradually with age. By age 70, CMV seropositivity ranged from 67% (blood donors) to 73% (SOT recipients). Significant proportions of women of child-bearing age were CMV-seronegative (organ donors, 44%; SOT recipients, 43%; blood donors, 61%). Blood donor CMV seroprevalence decreased from 48% in Western Canada to 30% in Eastern Canada. Women were more likely to be CMV-seropositive (ORs = 1.58, 1.45, and 1.11 for organ donors, SOT recipients, and blood donors, respectively) and EBV-seropositive (ORs = 1.87 and 1.46 for organ donors and SOT recipients, respectively). EBV prevalence rises rapidly, and by age 17-29 years, 81% of SOT recipients and 90% of organ donors were seropositive. Conclusions: Canada has relatively low and perhaps decreasing age-specific EBV and CMV prevalence, making Canadians vulnerable to primary infection-associated morbidity and suggesting benefit from future vaccines. Collection and analysis of routine serology screening data are useful for observing trends.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".