Homologación de vacunas contra la COVID-19: experiencia y desafíos desde su implementación en Chile
Bibliographic record
Abstract
The objective of this article is to describe the experience in Chile, during 2021 and 2022, with the validation of COVID-19 vaccines administered abroad and the main obstacles during the implementation of this process. This validation is given throughout South America and, in the case of Chile, it has been a successful undertaking with the validation of more than two million vaccines from different countries. Validation is a systematic process involving reviews conducted by trained professionals, which helps maintain international relations with other countries and fulfill the objectives set forth by the health authority. Despite the project's success, it has brought to light situations such as digital gaps in the population and differences in the reporting systems and types of vaccines administered in each country. The following solutions have been proposed: a public contact center for users having difficulty with the technology; more flexible requirements for validation; and the possibility of continuing with the vaccination program in Chile, always focused on protecting the population, reducing the potential risk of disease transmission, and maintaining public health.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".