Participant-centred active surveillance of adverse events following immunisation: a narrative review
Bibliographic record
Abstract
The importance of active, participant-centred monitoring of adverse events following immunisation (AEFI) is increasingly recognised as a valuable adjunct to traditional passive AEFI surveillance. The databases OVID Medline and OVID Embase were searched to identify all published articles referring to AEFI. Only studies which sought participant response after vaccination were included. A total of 6060 articles published since the year 2000 were identified. After the application of screening inclusion and exclusion criteria, 25 articles describing 23 post-marketing AEFI systems were identified. Most countries had a single system: Ghana, Japan, China, Korea, Netherlands, Singapore, Brazil, Cambodia, Sri Lanka, Turkey and Cameroon except the USA (2), Canada (4) and Australia (6). Data were collected from participants with and without AEFI in all studies reviewed with denominator data enabling AEFI rate calculations. All studies considered either a single vaccine or specified vaccines or were time limited except one Australian system, which provides continuous automated participant-centred active surveillance of all vaccines. Post-marketing surveillance systems using solicited patient feedback are emerging as a novel AEFI monitoring tool. A number of exploratory systems utilising e-technology have been developed and their potential for scaling up and application in low and middle income countries deserves further investigation.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".