Report from the World Health Organization’s immunization and vaccines related implementation research advisory committee (IVIR-AC) meeting, Geneva, 11–13 September 2023
Bibliographic record
Abstract
Evaluating vaccine-related research is critical to maximize the potential of vaccination programmes. The WHO Immunization and Vaccine-related Implementation Research Advisory Committee (IVIR-AC) provides an independent review of research that estimates the performance, impact and value of vaccines, with a particular focus on transmission and economic modelling. On 11-13 September 2023, IVIR-AC was convened for a bi-annual meeting where the committee reviewed research and presentations across eight different sessions. This report summarizes the background information, proceedings and recommendations from that meeting. Sessions ranged in topic from timing of measles supplementary immunization activities, analyses of conditions necessary to meet measles elimination in the South-East Asia region, translating modelled evidence into policy, a risk-benefit analysis of dengue vaccine, COVID-19 scenario modelling in the African region, therapeutic vaccination against human papilloma virus, the Vaccine Impact Modelling Consortium, and the Immunization Agenda 2030 vaccine impact estimates.
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 | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.024 |
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".