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Objectives: This study aimed to perform a systematic review and meta‐analysis regarding the prevalence of traumatic dental injuries (TDI) in emergency dental services. Methods: Seven electronic and two grey literature databases were searched, up to December 2021, for studies that reported the TDI prevalence among dental emergencies. The Joanna Briggs Institute critical appraisal checklist for prevalence studies, and The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach were used to assess the risk of bias and quality of evidence respectively. The R software was used to perform a proportion meta‐analysis on a random‐effects model to estimate the pooled prevalence and respective 95% CI. Results: From a total of 1476 studies identified after excluding duplicates, 38 met the inclusion criteria, and another five were identified through hand searching, summing 43 included observational studies with a total sample of 209099 individuals searching for emergency dental care. The overall pooled prevalence of TDI was 15.4% (95% CI: 11%‐21%, I² = 100%). Paediatric dental emergency services and age group from 0 to 21 years presented the highest prevalence, 29% (95% CI: 22%‐38%, I² = 99.5%) and 24% (95% CI: 15%‐35%, I² = 98%) respectively. Conclusions: The overall pooled prevalence of TDI in emergency dental services was 15.4%, and 24% in the age group under 21 years. Considering the impact of prompt and correct emergency care in the long‐term prognosis of TDI, such knowledge is relevant to organize emergency healthcare and support public policies in this area. [ABSTRACT FROM AUTHOR]

In survival analysis sometimes the interest is not just on the failure time distribution function. It is common treatment comparisons as well as studying the effect of covariates in the response. This fact happens in engineering and clinical studies. Covariates effects are usually incorporated in the analysis by using a regression model. Moreover, in some real situations, covariates may be monitored and measured along the follow-up period. These covariates are known as time-dependent covariates. Analysis that include these covariates can be more reliable. Aalen proposed an additive risk model that is very attractive. This model has showed some practical advantages especially when the covariates effects varies in time. The main advantage of this model it is that through graphical analysis it is possible to verify changes in the time in the influence of each one of the covariates. That is, analyses with this model supply information detailed regarding the secular influence of each covariate. This model is presented in this paper in terms of time-dependent covariates. A real data set related to HIV patients and time to develop sinusitis is used to illustrate the fit of the additive Aalen model.En el análisis de supervivencia, a veces, el interés no es sólo la distribución del tiempo de fallo. Es habitual comparar los tiempos de supervivencia de dos o más grupos así como la verificación del efecto de covariables en la respuesta, especialmente en situaciones de ingeniería y en los estudios clínicos. La Los efectos de las covariables general se incorporan en el análisis mediante el uso de un modelo de regresión. Por otra parte, en algunas situaciones reales, las covariables pueden ser monitoreados y medidos a lo largo del período de seguimiento. Estas se denominan covariables dependientes del tiempo e incluirlas en el análisis puede proporcionar resultados más exactos. Aalen propuso un modelo de riesgo aditivo que tiene ventajas prácticas cuando las covariables son seguidas en el tiempo y sus valores pueden ser modificados durante el estudio. La principal ventaja de este modelo es que a través de análisis gráfico es posible verificar los cambios en el tiempo de la influencia de cada una de las covariables. Es decir, el análisis con este modelo proporciona información detallada acerca de la influencia temporal de cada covariable. Así, el modelo Aalen aditivo se presenta en este documento con el fin de mostrar su importancia en la presencia de covariables dependientes del tiempo. Una base de datos real de pacientes infectados por VIH y el tiempo para el desarrollo de la sinusitis se utiliza para ilustrar el ajuste del modelo aditivo de Aalen.Em estudos de sobrevivência, as vezes, o interesse não é apenas na distribuição do tempo de falha. É comum a comparação de tempos de sobrevivência de dois ou mais grupos bem como a verificação do efeito de covariáveis na resposta tanto em engenharia quanto, principalmente, em situações clínicas. A maneira mais eficiente de incorporar o efeito desses fatores no estudo é utilizar um modelo estatístico de regressão. Freqüentemente em estudos de sobrevivência o efeito das covariáveis de interesse pode variar ao longo dotempo de duração do estudo. Estas covariáveis são denominadas de dependentes do tempo e a inclusão delas na análise pode fornecer resultados mais precisos. Aalen propôs um modelo de risco aditivo que apresenta vantagens práticas quando as covariáveis são acompanhadas ao longo do tempo e os seus valores podem ser modificados durante o estudo. A principal vantagem desse modelo é que através de análise gráfica é possível verificar mudanças no tempo na influência de cada uma das covariáveis. Isto é, análises com este modelo fornecem informações detalhadas a respeito da influência temporal de cada covariável. Dessa forma o modelo aditivo de Aalen é apresentado neste trabalho com o objetivo de mostrar a sua importância na presença de covariáveis dependentes do tempo. Um banco de dados real envolvendo pacientes infectados pelo HIV e o tempo até o desenvolvimento de sinusite é utilizado para ilustrar o ajuste deste modelo.

Survival data involving silent events are often subject to interval censoring (the event is known to occur within a time interval) and classification errors if a test with no perfect sensitivity and specificity is applied. Considering the nature of this data plays an important role in estimating the time distribution until the occurrence of the event. In this context, we incorporate validation subsets into the parametric proportional hazard model, and show that this additional data, combined with Bayesian inference, compensate the lack of knowledge about test sensitivity and specificity improving the parameter estimates. The proposed model is evaluated through simulation studies, and Bayesian analysis is conducted within a Gibbs sampling procedure. The posterior estimates obtained under validation subset models present lower bias and standard deviation compared to the scenario with no validation subset or the model that assumes perfect sensitivity and specificity. Finally, we illustrate the usefulness of the new methodology with an analysis of real data about HIV acquisition in female sex workers that have been discussed in the literature. [ABSTRACT FROM AUTHOR]

In this study we introduce a likelihood‐based method, via the Weibull and piecewise exponential distributions, capable of accommodating the dependence between failure and censoring times. The methodology is developed for the analysis of clustered survival data and it assumes that failure and censoring times are mutually independent conditional on a latent frailty. The dependent censoring mechanism is accounted through the frailty effect and this is accomplished by means of a key parameter accommodating the correlation between failure and censored observations. The full specification of the likelihood in our work simplifies the inference procedures with respect to Huang and Wolfe since it reduces the computation burden of working with the profile likelihood. In addition, the assumptions made for the baseline distributions lead to models with continuous survival functions. In order to carry out inferences, we devise a Monte Carlo EM algorithm. The performance of the proposed models is investigated through a simulation study. Finally, we explore a real application involving patients from the Dialysis Outcomes and Practice Patterns Study observed between 1996 and 2015. [ABSTRACT FROM AUTHOR]

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Background: The same reference values for cortisol have been used for adults and children, but laboratory results obtained from an adult population might not be suitable for pediatric patients. Aims: To determine morning serum basal cortisol levels in children and adolescents. Methods: The study was conducted on 120 suitable reference individuals, healthy Brazilian children and adolescents from both genders aged 4-19 years old. The method used for cortisol assessment was the chemiluminescent enzyme immunoassay (VITROS® 5600 MicroWell; Johnson & Johnson, High Wycombe, UK, 2009), and the kit reference interval was 4.46-22.7 μg/dl (122.7-626.2 nmol/l). The setting limits were calculated according to the Clinical Laboratory Standards Institute guidelines. Results: The morning serum basal cortisol levels increased with age and pubertal maturation, but there were no differences based on gender. The cortisol reference values were established based on the 2.5th and 97.5th percentiles as 2.97 μg/dl [81.9 nmol/l, 90% confidence interval (CI) 1.44-3.69 μg/dl] and 23.4 μg/dl (645.5 nmol/l; 90% CI 16.3-26.4 μg/dl), respectively. Conclusions: The assessment of the morning serum basal cortisol levels showed a unique pattern, with a different lower limit for the cohort compared to current values established for adult subjects. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]