Proposta de gestão on-line das informações de vigilância epidemiológica de eventos adversos pós-vacinação
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,035 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,010 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».