COVID-19 vaccination: Why extend the interval between doses?
Bibliographic record
Abstract
On March 3, 2021, faced with ongoing morbidity and mortality from coronavirus disease 2019 (COVID-19) and insufficient supplies of authorized, available vaccines against COVID-19 in Canada, the National Advisory Committee on Immunization (NACI) issued a strong recommendation to allow for an extended interval between vaccine doses to maximize the number of people protected as quickly as possible. NACI's recommendation was released in the form of a rapid response because of the urgency of the situation and was based on a review of the evidence; principles of immunology; historical experience with vaccines; modelling studies; and consideration of ethics, equity, feasibility, and acceptability. Since then, many questions and concerns have been raised. This article aims to provide further explanation of the rationale for the decision and prepare health care providers with information they need as they support their patients in the vaccination rollout.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".