Bibliographic record
Abstract
Abstract: Based on their Gross National Income (GNI) per capita, the World Bank has categorized 80 economies as HighIncome. Global pharmacovigilance rules are primarily driven by three major regulatory stakeholders: the Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA). This article's goal is to provide an overview of pharmacovigilance systems and procedures in high-income nations, especially those that are also International Conference on Harmonization (ICH) members. Every high-income nation is a part of the WHO PIDM. Medication safety precautions are directly correlated with a nation's income level. The 10 intrepid members of the Uppsala Monitoring Center are from affluent nations and were among the first to act following the thalidomide catastrophe, establishing drug appraisal committees, launching ADR reporting forms, and implementing safety protocols. Although VigiBase is accessible, several nations have their own databases for data management and analysis, such as the FDA Adverse Event Reporting System, the French pharmacovigilance database, the EU's Eudravigilance system, and Canada's Vigilance online database. Strong pharmacovigilance systems are present in all high-income nations. The two international leaders in pharmacovigilance are the USFDA and EMA. The majority of wealthy nations adhere to EMA regulations. The degree of affluence in a nation directly affects the safety of medicines.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".