Evaluation of a real-time hospital surveillance system for respiratory syncytial virus, Ontario, Canada, 2022–2023
Bibliographic record
Abstract
Background: Respiratory syncytial virus (RSV) surged in the 2022-2023 respiratory season after low activity during the pandemic. To monitor the RSV season in real time and support healthcare planning, Ontario introduced daily hospital bed census reporting of RSV hospitalizations by age group (0-17, 18-64, 65 years and older). Objectives: To assess the completeness and quality of the newly introduced real-time surveillance compared to end-of-season ICD-10 coded hospitalization discharge abstract data (DAD) from November 22, 2022, to March 31, 2023. Methods: Respiratory syncytial virus hospitalizations from both data sources were compared to RSV laboratory positivity to assess concordance with overall RSV activity. A longitudinal comparison by age group was assessed by time-lagged cross-correlation of the daily submission data versus DAD data, including cross correlation coefficients for each time lag, confidence bound and the highest correlation value. Results: Both data sources followed trends in RSV positivity. Data by age groups showed an early peak of paediatric admissions followed by a peak in adult and older adult hospitalizations. Daily surveillance consistently underestimated hospitalizations with a peak of 430 beds by DAD on January 7, 2023, versus 322 beds (75%) for daily reporting on the same day. The maximum correlation coefficient values were 0.67 (all ages), 0.57 (0-17 years), 0.66 (18-64 years) and 0.63 (65 years and older). Conclusion: Implementation of daily hospital reporting provided accurate trending in RSV hospitalizations by age group to inform within season healthcare and public health planning.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".