The National Advisory Committee on Immunization (NACI): A celebration of fifty years of service
Bibliographic record
Abstract
The National Advisory Committee on Immunization (NACI) celebrates its fiftieth anniversary in October 2014.This paper outlines the history of NACI and its activities over the past 50 years.After its formation in 1964, NACI undertook the development of guidance for the few vaccines that were approved at that time, including polio, measles, tetanus, pertussis, diphtheria and smallpox.Although the Committee has evolved over the years, its focus has remained on providing guidance on an ever-increasing number of products: twenty-four, according to the recent updates to the Canadian Immunization Guide.With the changing vaccine landscape, including immunization programs and practices, the membership of the Committee has evolved to include expanded expertise, such as immunology.The work of the Committee has had to adapt, with more complex and extensive evidence and the establishment of a published evidence-based methodology.Over the past 50 years, the success and accomplishments of NACI have been in large part due to the efforts of its chair and members, who have dedicated countless hours of work to committee activities and products.NACI's Technical advisory groups are recognized globally as a valuable resource, benefiting immunization practices and programs across Canada and internationally.NACI is a committee of which PHAC and indeed Canada can be truly proud.
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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 | 0.055 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".