Immunization guidance products: Different levels of detail for different uses
Bibliographic record
Abstract
The National Advisory Committee on Immunization (NACI) provides expert and evidence-based advice to the Public Health Agency of Canada (PHAC) on the use of human vaccines in Canada.This advice is presented in a variety of publications for different uses.A recent survey identified some confusion regarding the various NACI publication products.The objective of this article is to identify the level of detail and appropriate uses of the different NACI products.NACI statements provide a synthesis of current evidence and expert opinion on new vaccines or new indications for vaccines to inform immunization practices, policies and programs.NACI literature reviews inform new NACI statements and are published after the statement to inform readers about current literature on a specific immunization topic.The Canadian Immunization Guide (CIG) is a practice-oriented guide that synthesizes all the NACI statements and is updated regularly.NACI statement summaries are published in the Canada Communicable Disease Report (CCDR) and provide a high level overview of these statements shortly after they are published.These products provide a variety of options for users to choose how in-depth they wish to explore the evidence base and process for producing recommendations for immunization in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".