Seroprevalence of Pandemic Influenza H1N1 in Ontario from January 2009–May 2010
Bibliographic record
Abstract
BACKGROUND: We designed a seroprevalence study using multiple testing assays and population sources to estimate the community seroprevalence of pH1N1/09 and risk factors for infection before the outbreak was recognized and throughout the pandemic to the end of 2009/10 influenza season. METHODS: Residual serum specimens from five time points (between 01/2009 and 05/2010) and samples from two time points from a prospectively recruited cohort were included. The distribution of risk factors was explored in multivariate adjusted analyses using logistic regression among the cohort. Antibody levels were measured by hemagglutination inhibition (HAI) and microneutralization (MN) assays. RESULTS: Residual sera from 3375 patients and 1024 prospectively recruited cohort participants were analyzed. Pre-pandemic seroprevalence ranged from 2%-12% across age groups. Overall seropositivity ranged from 10%-19% post-first wave and 32%-41% by the end of the 2009/10 influenza season. Seroprevalence and risk factors differed between MN and HAI assays, particularly in older age groups and between waves. Following the H1N1 vaccination program, higher GMT were noted among vaccinated individuals. Overall, 20-30% of the population was estimated to be infected. CONCLUSIONS: Combining population sources of sera across five time points with prospectively collected epidemiological information yielded a complete description of the evolution of pH1N1 infection.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".