Optimal implementation of an Ontario nirsevimab program for respiratory syncytial virus (RSV) prophylaxis: Recommendations from a provincial RSV expert panel
Bibliographic record
Abstract
In June 2024, a group of 12 experts in the respiratory syncytial virus (RSV) field representing a cross-section of healthcare provider types who treat and care for pregnant individuals and infants, assembled to discuss the implementation of a broad infant prophylaxis program with nirsevimab in Ontario. To gain insight on potential best practices founded on the experiences of other jurisdictions, the meeting comprised a review of the 2023/2024 RSV season programs in Spain, France, and the United States that implemented nirsevimab prophylaxis. The impact of nirsevimab in reducing severe RSV disease among infants during the first RSV season was assessed including the implications on hospital resources and healthcare system costs. The panel also reviewed the results of a 2024 online survey of healthcare providers in the province to gain insight into how the program should be implemented in Ontario to facilitate uptake in infants born during and before the onset of the RSV season. The resulting discussion led to panel consensus on several recommendations to help inform programmatic decisions regarding how nirsevimab should be administered to infants in Ontario to achieve optimal uptake and best protection against this potentially devastating infectious disease.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".