El papel del Comité Panamericano de Vacunación Segura (COPAVASE) en el fortalecimiento de la vacunación segura en las Américas
Bibliographic record
Abstract
represented one of the first steps toward building the regional system for surveillance of events supposedly attributable to vaccination or immunization (ESAVIs) and adverse events of special interest (AESIs). This manual establishes that, after notification and investigation of an event, a national committee of experts should classify the event in accordance with the World Health Organization (WHO) causality classification. The Pan American Committee for Safe Vaccination (COPAVASE) was created in response to the introduction of the new COVID-19 vaccines to support causality analysis of complex regional ESAVI cases and to advise the Pan American Health Organization (PAHO) on strategies for developing safety information and implementing risk mitigation measures. As part of this work, two strategic planning exercises were carried out, one with Committee members and PAHO staff and another that included national authorities and committee members, who contributed ideas on how to strengthen the work both at the regional level and in countries' surveillance systems.Suggested areas of work included definition of clear guidelines, development of model terms of reference and case presentation guidelines, training, and strategies to ensure committee sustainability.With the strategies identified, PAHO expects to be able to continue strengthening national safe vaccination committees as key institutions for maintaining public trust.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".