Proposta de gestão on-line das informações de vigilância epidemiológica de eventos adversos pós-vacinação
Bibliographic record
Abstract
Nowadays, a number of vaccines are able to protect people, reducing dramatically the incidence of diseases. To manage the immunizing actions in public health, the Brazilian National Immunization Program was created in 1973. Through its working mechanisms, such as, providing vaccines for the whole population, funded the Federal Government, without direct cost for vaccinees; storage, transportation and supply of vaccines in appropriate cold chain settings; reliable information systems, the National Immunization Program has succeed in its goal to control many diseases preventable by immunization. However, we know that the occurrence of adverse events may follow the administration of immunizing products – AEFI. To monitor and control AEFI, the Epidemiological Surveillance of Adverse Events Following Immunization was created by National Immunization Program in 1992. This service was structured to recognize and identify AEFI cases, subsidize research work, and support health professionals in surveillance, and other objectives that contribute to vaccines control, health and welfare of the population. To control adverse events, AEFI’s Epidemiological Surveillance use a notification form, monitoring manual with information and instructions to report and investigate AEFI’s cases and supply data to the information system. The latter is critical to follow up suspected and confirmed cases of AEFI, identifying severe cases, outbreaks and monitor vaccine lots that may cause adverse events to the vaccinated population. Since 1998, the National Immunization Program has managed the Adverse Events Following Immunization’s Informations System, developed by the technical staff in the Ministry of Health Department - DATASUS. Based on the guidelines and criteria for evaluation of the Surveillance Systems for the Centers for Disease Control and Prevention (CDC) – Atlanta / USA, several flaws and errors in systems were pointed out, and a proposal for a new information system was conceived to improve the effectiveness of the Epidemiological Surveillance of Adverse Events Following Immunization. The system was revised according to the standardization of Adverse Reactions Terminology (WHO-ART) and Medical Dictionary of Regulatory Activities (MedDRA) of the Network Uppsala Monitoring Center (UMC). The new informations system proposed in this dissertation may benefit the Epidemiological Surveillance of Adverse Events Following Immunization by expediting the flow of AEFI’s data, expanding the access to information to various health professionals, and to vaccine manufacturers, updating and facilitating operation, while mantaining security and privacy. This proposal include a new notification form based on the current format in use in the health units in the country besides the notification forms of Surveillance Systems in Canada and USA. The Epidemiological Surveillance Center of State Secretary for Health in Sao Paulo, also contributed to its model of form.
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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 | 0.035 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".