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Avaliação de seqüelas neurológicas em hanseníase no Estado de Sergipe

2017· article· en· W7008172507 sur OpenAlexaboutno aff

Notice bibliographique

RevueAmericanae (AECID Library) · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueLeprosy Research and Treatment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLeprosyEpidemiologyPublic healthDiseaseMycobacterium lepraeNeuritisLepromatous leprosy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Leprosy is a chronic disease caused by intimate and prolonged contact with contagious untreated patients. Mycobacterium leprae has a predilection for skin and peripheral nerves. In Brazil the disease is a public health problem and the State of Sergipe is priority to control. The most complication of leprosy is occurrence of reactional episode, when no treated properly, promotes progression of nerve damage. The overall strategy to control the disease burden is to reduce the degree 2 of disability over the next five years. Studies showing the epidemiology and geographical distribution of leprosy cases, as well as risk factors for the disease are needed to control actions. The objective of this study is to evaluate the leprosy in the Sergipe State, focusing neurological sequelae as well as evaluating the overall detection rate and geographical distribution of leprosy and the degree of disability and define clinical aspects associated with neurological lesion in patients in reference units of the State. This is a retrospective study with data from System of Notifiable Diseases (SINAN), Brazilian Institute of Geography and Statistics (IBGE) and analysis records of reference units between the years 2005 to 2011. The variables used were gender, age, clinical form, operational classification, physical disability at diagnosis and after treatment, reactional episodes, treatment with corticosteroids and number of cities with household clusters (5 and 9 people / home). Maps showing the geographical distribution of leprosy cases and the degree of neurological disability in the municipalities of the state were created by Spring program, version 5.1.8 and ArcGIS, version 9.3.1. Categorical variables were described in simple frequencies, percentages, and with association analysis, using the chi-square (95% significance). Quantitative variables were described as mean and standard deviation and the clinical variables associated with the "severity of illness" (multibacillary and / or reactive episodes) through the logistic regression model. The state has hyperendemic municipalities indicating that the disease remains a public health problem. In 2005, Ontario had a detection rate of 33.0/100, 000 inhabitants, followed by a gradual reduction in the number of new cases by the year 2010. However, at the same time, increase was recorded with disability. The cases of leprosy were found in counties that have clusters of homes with more than five people / home. We observed a significant association with male and multibacillary, leprosy reactions and disability at diagnosis. This predisposition to severe forms of leprosy in men may be due a delay in diagnosis and treatment. Reactional episodes were detected in 40% of patients and neurological injury in 43.5%. Most patients with leprosy reactions were treated with corticosteroids but dose and time used below the recommended treatment, maintaining and/or developed disability. The association between male gender and more severe forms of the disease suggests that this group needs more attention by leprosy programs as there is need for better conduct of treatment and monitoring of neurological damage in leprosy in order to prevent disability.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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.

Tête enseignante Opus0,019
Tête enseignante GPT0,295
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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