Établissement de paramètres et de seuils pour l'évaluation de la sévérité de la grippe pandémique (PISA) dans le cadre du programme canadien ÉpiGrippe
Bibliographic record
Abstract
Background: The World Health Organization (WHO) developed a structured framework to enable countries to rapidly assess the severity of an influenza pandemic.This framework, the Pandemic Influenza Severity Assessment (PISA), is intended to be performed weekly during seasonal epidemics so that assessing influenza severity during a pandemic can be done with greater ease and efficiency.Objective: Using influenza surveillance indicators within Canada's FluWatch program from seasons 2014-2015 to 2018-2019, national PISA thresholds were developed and assessed against seasonal data for seasons 2019-2020 to June of 2022-2023.Outcomes: Canada developed thresholds for each required indicator (transmissibility, seriousness of disease and impact) for multiple WHO-recommended parameters.The thresholds were assessed against four seasons, and it was determined that there was a good agreement between the PISA assessments and the characterization of the season by FluWatch epidemiologists.Conclusion: With confidence in the validity of the PISA thresholds, the FluWatch program will begin to share PISA assessments weekly through the FluWatch report in the 2023-2024 seasons to help characterize influenza activity in Canada and inform responses to the seasonal influenza epidemic.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".