Pharmacovigilance in High-Income Countries: Current Developments and a Review of Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The world bank has classified 80 economies based on their Gross National Income (GNI) per capita as High-Income. European Medicines Agency (EMA), Food and Drug Administration (FDA), and Pharmaceuticals and Medical Devices Agency (PMDA) are the major regulatory stakeholders driving global pharmacovigilance regulations. The purpose of this article is to describe pharmacovigilance systems and processes in high-income countries, particularly those that are also members of the International Conference on Harmonization (ICH). All high-income countries are members of the WHO PIDM. The income level of a country has a direct relationship with medicine safety measures. All ten pioneering members of the Uppsala monitoring centre are from high-income countries and were the first responders after the thalidomide tragedy by making drug evaluation committees, introducing the ADR reporting forms and taking safety measures. Despite access to the VigiBase, some countries have separate databases for managing and analyzing data like Canada Vigilance online database, FDA Adverse Event Reporting System, the French pharmacovigilance database and European Union's system Eudravigilance. All high-income countries have robust pharmacovigilance systems. USFDA and EMA are the world leaders in the field of pharmacovigilance. Most high-income countries follow EMA guidelines. Medicine safety is directly influenced by a country's income level.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it