Meta-análisis: Intervalos de confianza e Intervalos de predicción / Meta-analysis: Confidence intervals and Prediction intervals

En los informes meta-analíticos se suelen reportar varios tipos de intervalos, hecho que ha generado cierta confusión a la hora de interpretarlos. Los intervalos de confianza reflejan la incertidumbre relacionada con un número, el tamaño del efecto medio paramétrico. Los intervalos de predicción reflejan el tamaño paramétrico probable en cualquier estudio de la misma clase que los incluidos en un meta-análisis. Su interpretación y aplicaciones son diferentes. En este artículo explicamos su diferente naturaleza y cómo se pueden utilizar para responder preguntas específicas. Se incluyen ejemplos numéricos, así como su cálculo con el paquete metafor en R.(AU)

Several types of intervals are usually employed in meta-analysis, a fact that has generated some confusion when interpreting them. Confidence intervals reflect the uncertainty related to a single number, the parametric mean effect size. Prediction intervals reflect the probable parametric effect size in any study of the same class as those included in a meta-analysis. Its interpretation and applications are different. In this article we explain in de-tail their different nature and how they can be used to answer specific ques-tions. Numerical examples are included, as well as their computation with the metafor Rpackage.(AU)

Predictors of transition to schizophrenia and other long-lasting non-affective psychoses in first-episode patients with acute and transient psychotic disorders: A validation study

Background and objectives Almost half of the individuals with a first-episode of psychosis who initially meet criteria for acute and transient psychotic disorder (ATPD) will have had a diagnostic revision during their follow-up, mostly toward schizophrenia. This study aimed to determine the proportion of diagnostic transitions to schizophrenia and other long-lasting non-affective psychoses in patients with first-episode ATPD, and to examine the validity of the existing predictors for diagnostic shift in this population. Methods We designed a prospective two-year follow-up study for subjects with first-episode ATPD. A multivariate logistic regression analysis was performed to identify independent variables associated with diagnostic transition to persistent non-affective psychoses. This prediction model was built by selecting variables on the basis of clinical knowledge. Results Sixty-eight patients with a first-episode ATPD completed the study and a diagnostic revision was necessary in 30 subjects at the end of follow-up, of whom 46.7% transited to long-lasting non-affective psychotic disorders. Poor premorbid adjustment and the presence of schizophreniform symptoms at onset of psychosis were the only variables independently significantly associated with diagnostic transition to persistent non-affective psychoses. Conclusion Our findings would enable early identification of those inidividuals with ATPD at most risk for developing long-lasting non-affective psychotic disorders, and who therefore should be targeted for intensive preventive interventions. (AU)

Factors associated with neurofeedback and mindfulness-based combination therapy for patients with substance use disorder: A multicenter study

Background and objectives Substance use disorder (SUD) has become a major concern in public health globally, and there is an urgent need to develop an integrated psychosocial intervention. The aims of the current study are to test the efficacy of the integrated treatment with neurofeedback and mindfulness-based therapy for SUD and identify the predictors of the efficacy. Methods This study included 110 participants with SUD into the analysis. Outcome of measures includes demographic characteristics, severity of dependence, quality of life, symptoms of depression, and anxiety. Independent t test is used to estimate the change of scores at baseline and three months follow-up. Generalized estimating equations are applied to analyze the effect of predictors on the scores of dependence severity over time by controlling for the effects of demographic characteristics. Results A total of 22 (20 %) participants were comorbid with major mental disorder (MMD). The decrement of the severity in dependence, anxiety, and depression after treatment are identified. Improved scores of qualities of life in generic, psychological, social, and environmental domains are also noticed. After controlling for the effects of demographic characteristics, the predictors of poorer outcome are comorbid with MMD, lower quality of life, and higher level of depression and anxiety. Conclusion The present study implicates the efficacy of integrated therapy. Early identification of predictors is beneficial for healthcare workers to improve the treatment efficacy. (AU)

Exploring dental professionals' outlook on the future of dental care amidst the integration of artificial intelligence in dentistry: a pilot study in Pakistan.

OBJECTIVE: The purpose of this study is to explore the perspectives, familiarity, and readiness of dental faculty members regarding the integration and application of artificial intelligence (AI) in dentistry, with a focus on the possible effects on dental education and clinical practice. METHODOLOGY: In a mix-method cross-sectional quantitative and quantitative study conducted between June 1st and August 30th, 2023, the perspectives of faculty members from a public sector dental college in Pakistan regarding the function of AI were explored. This study used qualitative as well as quantitative techniques to analyse faculty's viewpoints on the subject. The sample size was comprised of twenty-three faculty members. The quantitative data was analysed using descriptive statistics, while the qualitative data was analysed using theme analysis. RESULTS: Position-specific differences in faculty familiarity underscore the value of individualized instruction. Surprisingly few had ever come across AI concepts in their professional lives. Nevertheless, many acknowledged that AI had the potential to improve patient outcomes. The majority thought AI would improve dentistry education. Participants suggested a few dental specialties where AI could be useful. CONCLUSION: The study emphasizes the significance of addressing in dental professionals' knowledge gaps about AI. The promise of AI in dentistry calls for specialized training and teamwork between academic institutions and AI developers. Graduates of dentistry programs who use AI are better prepared to navigate shifting environments. The study highlights the positive effects of AI and the value of faculty involvement in maximizing its potential for better dental education and practice.

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend.

BACKGROUND: Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. METHODS: We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. RESULTS: Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. CONCLUSION: Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition.

Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5.

Prediction model for future OHCAs based on geospatial and demographic data: An observational study.

This study used demographic data in a novel prediction model to identify areas with high risk of out-of-hospital cardiac arrest (OHCA) in order to target prehospital preparedness. We combined data from the nationwide Danish Cardiac Arrest Registry with geographical- and demographic data on a hectare level. Hectares were classified in a hierarchy according to characteristics and pooled to square kilometers (km2). Historical OHCA incidence of each hectare group was supplemented with a predicted annual risk of at least 1 OHCA to ensure future applicability. We recorded 19,090 valid OHCAs during 2016 to 2019. The mean annual OHCA rate was highest in residential areas with no point of public interest and 100 to 1000 residents per hectare (9.7/year/km2) followed by pedestrian streets with multiple shops (5.8/year/km2), areas with no point of public interest and 50 to 100 residents (5.5/year/km2), and malls with a mean annual incidence per km2 of 4.6. Other high incidence areas were public transport stations, schools and areas without a point of public interest and 10 to 50 residents. These areas combined constitute 1496 km2 annually corresponding to 3.4% of the total area of Denmark and account for 65% of the OHCA incidence. Our prediction model confirms these areas to be of high risk and outperforms simple previous incidence in identifying future risk-sites. Two thirds of out-of-hospital cardiac arrests were identified in only 3.4% of the area of Denmark. This area was easily identified as having multiple residents or having airports, malls, pedestrian shopping streets or schools. This result has important implications for targeted intervention such as automatic defibrillators available to the public. Further, demographic information should be considered when implementing such interventions.

Digital Health Education for the Future: The SaNuRN (Santé Numérique Rouen-Nice) Consortium's Journey.

SaNuRN is a five-year project by the University of Rouen Normandy (URN) and the Côte d'Azur University (CAU) consortium to optimize digital health education for medical and paramedical students, professionals, and administrators. The project includes a skills framework, training modules, and teaching resources. In 2027, SaNuRN is expected to train a significant portion of the 400,000 health and paramedical professions students at the French national level. Our purpose is to give a synopsis of the SaNuRN initiative, emphasizing its novel educational methods and how they will enhance the delivery of digital health education. Our goals include showcasing SaNuRN as a comprehensive program consisting of a proficiency framework, instructional modules, and educational materials and explaining how SaNuRN is implemented in the participating academic institutions. SaNuRN is a project aimed at educating and training health-related and paramedics students in digital health. The project results from a cooperative effort between URN and CAU, covering four French departments. The project is based on the French National Referential on Digital Health (FNRDH), which defines the skills and competencies to be acquired and validated by every student in the health, paramedical, and social professions curricula. The SaNuRN team is currently adapting the existing URN and CAU syllabi to FNRDH and developing short-duration video capsules of 20 to 30 minutes to teach all the relevant material. The project aims to ensure that the largest student population earns the necessary skills, and it has developed a two-tier system involving facilitators who will enable the efficient expansion of the project's educational outreach and support the students in learning the needed material efficiently. With a focus on real-world scenarios and innovative teaching activities integrating telemedicine devices and virtual professionals, SaNuRN is committed to enabling continuous learning for healthcare professionals in clinical practice. The SaNuRN team introduced new ways of evaluating healthcare professionals by shifting from a knowledge-based to a competencies-based evaluation, aligning with the Miller teaching pyramid and using the Objective Structured Clinical Examination and Script Concordance Test in digital health education. Drawing on the expertise of URN, CAU, and their public health and digital research laboratories and partners, the SaNuRN project represents a platform for continuous innovation, including telemedicine training and living labs with virtual and interactive professional activities. The SaNuRN project provides a comprehensive, personalized 30-hour training package for health and paramedical students, addressing all 70 FNRDH competencies. The program is enhanced using AI and NLP to create virtual patients and professionals for digital healthcare simulation. SaNuRN teaching materials are open-access. The project collaborates with academic institutions worldwide to develop educational material in digital health in English and multilingual formats. SaNuRN offers a practical and persuasive training approach to meet the current digital health education requirements.

Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction.

In this paper, we address the critical task of 24-h streamflow forecasting using advanced deep-learning models, with a primary focus on the transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including persistence, long short-term memory (LSTM), Seq2Seq, GRU, and transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash-Sutcliffe Efficiency (NSE), Pearson's r, and normalized root mean square error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. Specifically, the transformer model demonstrated a substantial improvement in NSE scores by up to 20% compared to other models. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction.

Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso.

With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on daily-scale streamflow and explores the application of the Lasso feature selection method alongside three machine learning models (long short-term memory, LSTM; transformer for time series, TTS; random forest, RF) in short-term streamflow prediction. Through comparative experiments, we found that the Lasso method significantly enhances the model's performance, with a respective increase in the generalization capabilities of the three models by 21, 12, and 14%. Among the selected features, lagged streamflow and precipitation play dominant roles, with streamflow closest to the prediction date consistently being the most crucial feature. In comparison to the TTS and RF models, the LSTM model demonstrates superior performance and generalization capabilities in streamflow prediction for 1-7 days, making it more suitable for practical applications in hydrological forecasting in the Haihe River Basin and similar regions. Overall, this study deepens our understanding of feature selection and machine learning models in hydrology, providing valuable insights for hydrological simulations under the influence of complex human activities.

Projections of Single-level and Multilevel Spinal Instrumentation Procedure Volume and Associated Costs for Medicare Patients to 2050.

BACKGROUND: Instrumented spinal fusions can be used in the treatment of vertebral fractures, spinal instability, and scoliosis or kyphosis. Construct-level selection has notable implications on postoperative recovery, alignment, and mobility. This study sought to project future trends in the implementation rates and associated costs of single-level versus multilevel instrumentation procedures in US Medicare patients aged older than 65 years in the United States. METHODS: Data were acquired from the Centers for Medicare & Medicaid Services from January 1, 2000, to December 31, 2019. Procedure costs and counts were abstracted using Current Procedural Terminology codes to identify spinal level involvement. The Prophet machine learning algorithm was used, using a Bayesian Inference framework, to generate point forecasts for 2020 to 2050 and 95% forecast intervals (FIs). Sensitivity analyses were done by comparing projections from linear, log-linear, Poisson and negative-binomial, and autoregressive integrated moving average models. Costs were adjusted for inflation using the 2019 US Bureau of Labor Statistics' Consumer Price Index. RESULTS: Between 2000 and 2019, the annual spinal instrumentation volume increased by 776% (from 7,342 to 64,350 cases) for single level, by 329% (from 20,319 to 87,253 cases) for two-four levels, by 1049% (from 1,218 to 14,000 cases) for five-seven levels, and by 739% (from 193 to 1,620 cases) for eight-twelve levels (P < 0.0001). The inflation-adjusted reimbursement for single-level instrumentation procedures decreased 45.6% from $1,148.15 to $788.62 between 2000 and 2019, which is markedly lower than for other prevalent orthopaedic procedures: total shoulder arthroplasty (-23.1%), total hip arthroplasty (-39.2%), and total knee arthroplasty (-42.4%). By 2050, the number of single-level spinal instrumentation procedures performed yearly is projected to be 124,061 (95% FI, 87,027 to 142,907), with associated costs of $93,900,672 (95% FI, $80,281,788 to $108,220,932). CONCLUSIONS: The number of single-level instrumentation procedures is projected to double by 2050, while the number of two-four level procedures will double by 2040. These projections offer a measurable basis for resource allocation and procedural distribution.

Key factors in the future of oral and dental health in Iran using scenario writing approach.

BACKGROUND: Oral and dental health can significantly impact individuals' quality of life. The World Health Organization introduces oral health as one of the essential priorities of public health worldwide. Given the lack of studies on the future of oral and dental health in Iran, this study used a futures studies approach to identify the factors in oral and dental health in Iran through scenario writing. METHODS: This study was conducted in three stages including the scenario writing approach, qualitative methods, and exploratory future research. First, potential variables affecting future oral and dental health systems were extracted through interviews. The focus group discussion determined the uncertainty and importance of the variables. Then, the cross-impact balance matrix was imported into the Scenario Wizard software to identify the different states of the scenario generator variables and compatible scenarios were extracted. RESULTS: Seventy variables were extracted as key variables affecting the future of oral and dental health. Regarding the importance and uncertainty, seventeen variables scored higher and fell into policy and governance, economy and financing, social, service delivery, and technology, serving as five categories of scenario generators. Fifteen scenarios with weak consistency and three with strong consistency were obtained using the Cross-Impact Balance matrix in Scenario Wizard software. CONCLUSION: The probability of a pessimistic scenario where all five categories of the scenarios were in the worst possible state was higher due to its consistency. The government's support policies and commitment to oral and dental health were two key factors in the future. Achieving an optimistic and favorable scenario for the future of the country's oral and dental health system depends on the government and policymakers in the health sector adopting a positive attitude towards the role of oral and dental health services in improving societal health. In this scenario, the five categories of the scenario generators were in the best condition.

Global burden of liver cirrhosis 1990-2019 and 20 years forecast: results from the global burden of disease study 2019.

BACKGROUND: Cirrhosis is a disease that imposes a heavy burden worldwide, but its incidence varies widely by region. Therefore, we analysed data on the incidence and mortality of cirrhosis in 204 countries and territories from 1990-2019 and projected the disease development from 2019-2039. METHODS: Data on the incidence and mortality of liver cirrhosis from 1990 to 2019 were acquired from the public Global Burden of Disease (GBD) study. In addition, the average annual percentage change (AAPC) and estimated annual percentage change (EAPC) of the age-standardized rate (ASR) of cirrhosis in different regions were calculated. The estimates of risk factor exposure were summarized, and the proportion of causes and risk factors of liver cirrhosis and their relationship with the human development index (HDI) and socio-demographic index (SDI) were analysed. Trends in the incidence of cirrhosis in 2019-2039 were predicted using Nordpred and BAPC models. RESULTS: Globally, the ASR of cirrhosis incidence decreased by 0.05% per year from 25.7/100,000 in 1990 to 25.3/100,000 in 2019. The mortality risk associated with cirrhosis is notably lower in females than in males (13 per 100,000 vs 25 per 100,000). The leading cause of cirrhosis shifted from hepatitis B to C. Globally, alcohol use increased by 14%. In line, alcohol use contributed to 49.3% of disability-adjusted life years (DALYs) and 48.4% of global deaths from liver cirrhosis. Countries with a low ASR in 1990 experienced a faster increase in cirrhosis, whereas in 2019, the opposite was observed. In countries with high SDI, the ASR of cirrhosis is generally lower. Finally, projections indicate that the number and incidence of cirrhosis will persistently rise from 2019-2039. CONCLUSIONS: Cirrhosis poses an increasing health burden. Given the changing etiology, there is an imperative to strengthen the prevention of hepatitis C and alcohol consumption, to achieve early reduce the incidence of cirrhosis.

This study is an updated assessment of liver cirrhosis prevalence trends in 204 countries worldwide and the first to project trends over the next 20 years.The disease burden of cirrhosis is still increasing, and despite the decline in ASR, the number and prevalence of cirrhosis will continue to increase over the next two decades after 2019.It is alarming that the global surge in alcohol use is accompanied by an increase in DALYs and deaths due to liver cirrhosis.Liver cirrhosis remains a noteworthy public health event, and our study can further guide the development of national healthcare policies and the implementation of related interventions.

[The bibliography group, between dream and reality in nursing practice]. / Le groupe de bibliographie, entre rêve et réalité de l'exercice infirmier.

The shock of reality that nursing students face when they start out will affect the nursing profession even more in the future, as it faces a recruitment crisis in the midst of renewal. Restoring meaning to the nursing profession is a complex and daunting challenge. By providing access to scientific literature, the bibliography group can contribute to this, based on an Evidence-Based Nursing approach. This initiative, which is beneficial for professionals whose skills development is thus encouraged, is designed to be simple and accessible to as many people as possible.