Global alignment of immunization safety assessment in pregnancy – The GAIA project
Bibliographic record
Abstract
Immunization in pregnancy provides a promising contribution to globally reducing neonatal and under-five childhood mortality and morbidity. Thorough assessment of benefits and risks for the primarily healthy pregnant women and their unborn babies is required. The GAIA project was formed in response to the call of the World Health Organization for a globally concerted approach to actively monitor the safety of vaccines and immunization in pregnancy programs. GAIA aims to improve the quality of outcome data from clinical vaccine trials in pregnant women with a specific focus on the needs and requirements for safety monitoring in LMIC. In the first year of the project, a large and functional network of experts was created. The first outputs include a guidance document for clinical trials of immunization in pregnancy, a basic data collection guide, ten case definitions of key obstetric and neonatal health outcomes, an ontology of key terms and a map of pertinent disease codes. The GAIA Network is designed as an open and growing forum for professionals sharing the GAIA vision and aim. Based on the initial achievements, tools and services are developed to support investigators and strengthen immunization in pregnancy programs with specific focus on LMIC.
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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 | 0.045 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".