Consultation on interactions between National Regulatory Authorities and National Immunization Technical Advisory Groups
Bibliographic record
Abstract
A consultation during the Developing Country Vaccine Regulators' Network meeting of May 2010 considered the interactions between the National Immunization Technical Advisory Group (NITAG) and the National Regulatory Authority (NRA) in various countries. This meeting was co-hosted by the WHO and the Supporting Independent Immunization and Vaccine Advisory Committees Initiative implemented by the Agence de Médecine Préventive in partnership with the International Vaccine Institute. Representatives from Developing Country Vaccine Regulators' Network and representatives from several additional countries' regulatory authorities met representatives from NITAGs and/or the National Immunization Program from these countries (Brazil, Canada, China, Cuba, France, Indonesia, Iran, South Africa, Thailand, Vietnam and the USA). The objectives of the workshop included a discussion on the issues of NRA-NITAG interaction, the assessment of the advantages of different models of interaction and proposals for an optimal coordination process for market authorization and recommendations for use of vaccines. It was concluded that there is need for increased and more formal interactions between NRAs and NITAGs, a clear framework establishing a formal interaction and early interactions before market authorization. NRA experts being at the same time NITAG ex officio members and vice versa are solutions which can be adopted by countries. The NRA issues the license based on the evidence submitted by the manufacturer. The NITAG makes recommendations based on scientific evidence, public health needs and policy, and consideration of the license conditions. If there is a need to make recommendations that are not covered by the license evidence then there should be interactions between NITAG, NRA and the license holder to encourage the license-holder to submit appropriate evidence, or to ensure that the justification for the off-label recommendation is communicated to the users of the medicine.
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 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 | 0.001 |
| 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 | 0.000 |
| 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".