The 2009 Provincial Decision to De-emphasize Seasonal Influenza Vaccine in Canada: Real-Time Risk-Benefit Analysis
Bibliographic record
Abstract
TO THE EDITOR—In his editorial commentary [1], Glezen did not accurately reflect the full risk-benefit analysis undertaken by provinces of Canada in their decision to deemphasize the 2009 seasonal influenza vaccine program, and in addition he misrepresents the immunization policy process followed in Canada [1]. Findings during the spring-summer 2009 that showed seasonal influenza vaccine was associated with ∼2-fold increased risk of pandemic illness were unexpected [2, 3]. However, their consistency across multiple studies (6 in total by the fall 2009) in Canada lent a credibility that could not be dismissed [2, 3]. These findings motivated urgent reanalysis of anticipated seasonal influenza vaccine benefits versus risks in Canada. They were, however, not the sole or even main determinant of provincial program decisions. In response to these studies, provinces sought expert input through national pandemic and immunization advisory committees, but ultimately health care decisions are under provincial jurisdiction. The statement issued by the Canadian Agency for Drugs and Technologies in Health (CADTH) cited by Glezen summarized provincial decisions that were reached, but that document did not constitute the evidence base for those decisions [1]. CADTH does not play a role in Canadian decision making in this arena.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".