Biovigilance systems: Cells, tissues, and organs donation and transplantation
Bibliographic record
Abstract
Objective: to describe Biovigilance Systems and their associated management tools among member countries of the World Health Organization. Method: overview conducted following the population, concept, and context strategy to develop the research question and objective. Structured searches were conducted in PubMed, CINAHL, Embase, and Scopus. Snowballing procedure in Google Scholar and health authorities’ websites as World Health Organization and Pan American Health Organization during the first semester of 2023. Language and time restrictions were not applied. Results: we examined more than 70 studies and non-scientific works. Biovigilance systems were identified in 12 countries members of WHO in 3 of 6 regions: Pan-American Region (Brazil and Colombia, Canada), Europe (England, France, Germany, Italy, Netherlands, Poland, Portugal, and Spain), and Western Pacific Region (Australia). Conclusion: This overview achieved its objective by describing biovigilance systems and their management tools among World Health Organization member countries. This research, designed as an overview, refrains from generalizing results but holds significance for countries and health authorities developing biovigilance systems, offering benchmark opportunities and supporting system improvement. The study contributes directly to the biovigilance discourse, guiding efforts to enhance safety and quality globally.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".