Invasive pneumococcal disease surveillance in Canada, 2021–2022
Bibliographic record
Abstract
Background: ) has been a nationally notifiable disease in Canada since 2000. The use of conjugate vaccines has caused a shift in the distribution of serotypes over time. This report is a summary of the demographics, serotypes and antimicrobial resistance of IPD isolates collected in Canada in 2021 and 2022. Methods: The National Microbiology Laboratory (NML) of the Public Health Agency of Canada in Winnipeg, Manitoba collaborates with provincial and territorial public health laboratories to conduct national surveillance of IPD. There were 1,999 isolates reported in 2021 and 3,775 isolates in 2022. Serotype was determined by the Quellung reaction or whole-genome sequencing (WGS). Antimicrobial susceptibilities were determined by WGS methods, broth microdilution, or data shared by collaborators in the Canadian Antimicrobial Resistance Alliance program at the University of Manitoba. Population-based IPD incidence rates were obtained through the Canadian Notifiable Disease Surveillance System. Results: <0.05). Conclusion: The number of cases of IPD continued to decrease in 2021 in comparison to previous years, however, 2022 saw a return to pre-COVID-19 levels. Disease due to PCV13 serotypes 3, 4, 9V and 19F, as well as non-PCV13 serotypes 12F and 20, is increasing in prevalence. Surveillance of IPD to monitor changing serotype distribution and antimicrobial resistance is essential.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".