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)

Romanian Surgery between Tradition and Innovation: The Priorities of the 2024-2028 Mandate at the Romanian Society of Surgery.

img src="/images/viorel_scripcariu.jpg" alt="Viorel Scripcariu" style="float: right;max-width: 30%;"/ Assuming the leadership of the Romanian Society of Surgery for the next four years is for me a moment of deep significance and responsibility. With a tradition of almost 130 years, this organization is at the foundation of the Romanian surgeon community, and my role is to continue this legacy and open new paths for the future of Romanian surgery. I would like to share the priorities I have set for this mandate, given our shared commitment to promote excellence in surgical practice and to respond to contemporary challenges. An essential aspect of my mandate is to respect the rich tradition and history of Romanian surgery. The Romanian Society of Surgery was founded at the end of the 19th century in Bucharest, with the surgeon Constantin Severeanu at its head. At the meeting on January 27, 1899, the society constituted its first board, with Thoma Ionescu as president, together with other founding members such as Dr. Leonte, Dr. Racoviceanu-Pitesti, Dr. Duma and Dr. Staicovici. Over the years, numerous meetings were organized at the Institute of Topographic Anatomy in Bucharest, where clinical cases, new surgical methods and lectures by medical personalities from abroad were presented. Over the years, the society has continued to promote international collaboration, organizing congresses and inviting renowned surgeons from abroad to lecture and perform innovative surgery in Romania. This rich history and respect for traditional values are fundamental to preserving and enhancing the standards of excellence we have inherited. We will continue our efforts to honor our mentors and their achievements, ensuring that their legacy will continue to inspire and guide new generations of Romanian surgeons. [ a href="https://revistachirurgia.ro/pdfs/2024-4-357.pdf" read more /a ].

Evolution of treatment for unspecific back pain: From past to future.

Unspecific back pain (UBP) has long puzzled medical professionals. Historically, back pain (BP) was often attributed to mystical causes, treated with incantations or herbal concoctions. The Middle Ages shifted towards empirical practices, though still intertwined with superstition, using methods like leeches and bloodletting. The Renaissance introduced systematic healthcare approaches, laying the foundation for modern medicine. The 20th century saw significant advancements with diagnostic imaging, pharmacotherapy, physical therapy, and surgical interventions, though UBP remained elusive. Recent decades have seen a paradigm shift towards multidisciplinary approaches, addressing BP's multifactorial nature through holistic methods considering biomechanical, psychosocial, and lifestyle factors. This shift integrates quantitative research with hermeneutic interpretation, emphasizing evidence-based guidelines. Non-pharmacological interventions such as exercise therapy, electrotherapy, cognitive behavioral therapy, and mindfulness-based stress reduction have gained prominence, empowering individuals in their recovery. Technological innovations like virtual reality and artificial intelligence offer personalized treatment plans, optimizing outcomes. The future of BP treatment holds promise with advancements in regenerative medicine, neuromodulation, telemedicine, and remote monitoring platforms, enhancing accessibility and continuity of care, especially in underserved communities. However, challenges such as the opioid epidemic and healthcare disparities remain, necessitating judicious prescribing practices and equitable resource distribution. The evolving treatment landscape for UBP reflects the dynamic interplay between scientific progress, clinical innovation, and societal needs, aiming to alleviate the burden of back pain and improve quality of life.

A novel statistical framework of drought projection by improving ensemble future climate model simulations under various climate change scenarios.

Unlike other natural disasters, drought is one of the most severe threats to all living beings globally. Due to global climate change, the frequency and duration of droughts have increased in many parts of the world. Therefore, accurate prediction and forecasting of droughts are essential for effective mitigation policies and sustainable research. In recent research, the use of ensemble global climate models (GCMs) for simulating precipitation data is common. The objective of this research is to enhance the multi-model ensemble (MME) for improving future drought characterizations. In this research, we propose the use of relative importance metric (RIM) to address collinearity effects and point-wise discrepancy weights (PWDW) in GCMs. Consequently, this paper introduces a new statistical framework for weighted ensembles called the discrepancy-enhanced beta weighting ensemble (DEBWE). DEBWE enhances the weighted ensemble data of precipitation simulated by multiple GCMs. In DEBWE, we addressed uncertainties in GCMs arising from collinearity and outliers. To evaluate the effectiveness of the proposed weighting framework, we compared its performance with the simple average multi-model ensemble (SAMME), Taylor skill score ensemble (TSSE), and mutual information ensemble (MIE). Based on the Kling-Gupta efficiency (KGE) metric, DEBWE outperforms all competitors across all evaluation criteria. These inferences are based on the analysis of historical simulated data from 22 GCMs in the CMIP6 project. The quantitative performance indicators strongly support the superiority of DEBWE. The median and mean KGE values for DEBWE are 0.2650 and 0.2429, compared to SAMME (0.1000, 0.0991), TSSE (0.2600, 0.2397), and MIE (0.1550, 0.1511). For drought assessment, we computed the adaptive standardized precipitation index (SPI) for three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The steady-state probabilities suggest that normal drought (ND) is the most frequent condition, with extreme events (dry or wet) being less probable.

Forecasting carbon dioxide emissions in Chongming: a novel hybrid forecasting model coupling gray correlation analysis and deep learning method.

Predicting regional carbon dioxide (CO2) emissions is essential for advancing toward global carbon neutrality. This study introduces a novel CO2 emissions prediction model tailored to the unique environmental, economic, and energy consumption of Shanghai Chongming. Utilizing an innovative hybrid approach, the study first applies grey relational analysis to evaluate the influence of economic activity, natural conditions, and energy consumption on CO2 emissions. This is followed by the implementation of a dual-channel pooled convolutional neural network (DCNN) that captures both local and global features of the data, enhanced through feature stacking. Gated recurrent unit (GRU) network then assesses the temporal aspects of these features, culminating in precise CO2 emission predictions for the region. The results indicate: (1) The proposed hybrid model achieves accurate predictions based on accounting data, with high precision, low error, and good stability. (2) The study found an overall increase in Chongming's carbon emissions from 2000 to 2022, with the prediction results being generally consistent with existing research findings. (3) The proposed method, based on Chongming's CO2 emission predictions, addresses issues such as the scarcity of effective accounting data and inaccuracies in traditional calculation methods. The results can provide effective technical support for local government policies on carbon reduction and promote sustainable development.

Shaping the future of heart health.

For World Heart Day on September 24, 2024, the World Heart Federation urges nations to endorse national strategies for enhancing cardiovascular health. While advancements show promise in reducing atherosclerosis, addressing healthcare inequalities and ensuring equitable access to tools remain crucial. This collection of voices touches on the intricate relationship between type 2 diabetes and cardiovascular disease, highlights innovative treatments for rare cardiomyopathies and heart failure, and explores the potentially transformative role of artificial intelligence in cardiovascular medicine, showcasing the dedication and innovation that are shaping the future of heart health.

Computational surgery in the management of patients with abdominal aortic aneurysms: Opportunities, challenges, and future directions.

Computational surgery (CS) is an interdisciplinary field that uses mathematical models and algorithms to focus specifically on operative planning, simulation, and outcomes analysis to improve surgical care provision. As the digital revolution transforms the surgical work environment through broader adoption of artificial intelligence and machine learning, close collaboration between surgeons and computational scientists is not only unavoidable, but will become essential. In this review, the authors summarize the main advances, as well as ongoing challenges and prospects, that surround the implementation of CS techniques in vascular surgery, with a particular focus on the care of patients affected by abdominal aortic aneurysms (AAAs). Several key areas of AAA care delivery, including patient-specific modelling, virtual surgery simulation, intraoperative imaging-guided surgery, and predictive analytics, as well as biomechanical analysis and machine learning, will be discussed. The overarching goals of these CS applications is to improve the precision and accuracy of AAA repair procedures, while enhancing safety and long-term outcomes. Accordingly, CS has the potential to significantly enhance patient care across the entire surgical journey, from preoperative planning and intraoperative decision making to postoperative surveillance. Moreover, CS-based approaches offer promising opportunities to augment AAA repair quality by enabling precise preoperative simulations, real-time intraoperative navigation, and robust postoperative monitoring. However, integrating these advanced computer-based technologies into medical research and clinical practice presents new challenges. These include addressing technical limitations, ensuring accuracy and reliability, and managing unique ethical considerations associated with their use. Thorough evaluation of these aspects of advanced computation techniques in AAA management is crucial before widespread integration into health care systems can be achieved.

Estimating future climate change impacts on human mortality and crop yields via air pollution.

Future climate change may bring local benefits or penalties to surface air pollution, resulting from changing temperature, precipitation, and transport patterns, as well as changes in climate-sensitive natural precursor emissions. Here, we estimate the climate penalties and benefits at the end of this century with regard to surface ozone and fine particulate matter (PM[Formula: see text]; excluding dust and smoke) using a one-way offline coupling between a general circulation model and a global 3-D chemical-transport model. We archive meteorology for the present day (2005 to 2014) and end of this century (2090 to 2099) for seven future scenarios developed for Phase 6 of the Coupled Model Intercomparison Project. The model isolates the impact of forecasted anthropogenic precursor emission changes versus that of climate-only driven changes on surface ozone and PM[Formula: see text] for scenarios ranging from extreme mitigation to extreme warming. We then relate these changes to impacts on human mortality and crop production. We find ozone penalties over nearly all land areas with increasing warming. We find net benefits due to climate-driven changes in PM[Formula: see text] in the Northern Extratropics, but net penalties in the Tropics and Southern Hemisphere, where most population growth is forecast for the coming century.

Framework for multivariate carbon price forecasting: A novel hybrid model.

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Prediction of acute onset of chronic cor pulmonale: comparative analysis of Holt-Winters exponential smoothing and ARIMA model.

BACKGROUND: The aim of this study is to analyze the trend of acute onset of chronic cor pulmonale at Chenggong Hospital of Kunming Yan'an Hospital between January 2018 and December 2022.Additionally, the study will compare the application of the ARIMA model and Holt-Winters model in predicting the number of chronic cor pulmonale cases. METHODS: The data on chronic cor pulmonale cases from 2018 to 2022 were collected from the electronic medical records system of Chenggong Hospital of Kunming Yan'an Hospital. The ARIMA and Holt-Winters models were constructed using monthly case numbers from January 2018 to December 2022 as training data. The performance of the model was tested using the monthly number of cases from January 2023 to December 2023 as the test set. RESULTS: The number of acute onset of chronic cor pulmonale in Chenggong Hospital of Kunming Yan'an Hospital exhibited a downward trend overall from 2018 to 2022. There were more cases in winter and spring, with peaks observed in November to December and January of the following year. The optimal ARIMA model was determined to be ARIMA (0,1,1) (0,1,1)12, while for the Holt-Winters model, the optimal choice was the Holt-Winters multiplicative model. It was found that the Holt-Winters multiplicative model yielded the lowest error. CONCLUSION: The Holt-Winters multiplicative model predicts better accuracy. The diagnosis of acute onset of chronic cor pulmonale is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors' influence and try to incorporate them into the models.

Spatial and temporal analysis and forecasting of TB reported incidence in western China.

OBJECTIVE: Tuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies. METHODS: We collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China. RESULTS: The reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran's I was greater than 0 (p < 0.05) over 8 years during the reporting period. Among them, Tibet was the hotspot for TB incidence in 12 provinces and municipalities in western China. The reported incidence of TB in 12 provinces and municipalities in western China from 2004 to 2018 showed clear seasonal characteristics, with seasonal indices greater than 100% in both the first and second quarters. The optimal models constructed for TB reported incidence in 12 provinces and municipalities in western China all passed white noise test (p > 0.05). CONCLUSIONS: As a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different.

Forecasting extremely high ischemic stroke incidence using meteorological time serie.

MOTIVATION: The association between weather conditions and stroke incidence has been a subject of interest for several years, yet the findings from various studies remain inconsistent. Additionally, predictive modelling in this context has been infrequent. This study explores the relationship of extremely high ischaemic stroke incidence and meteorological factors within the Slovak population. Furthermore, it aims to construct forecasting models of extremely high number of strokes. METHODS: Over a five-year period, a total of 52,036 cases of ischemic stroke were documented. Days exhibiting a notable surge in ischemic stroke occurrences (surpassing the 90th percentile of historical records) were identified as extreme cases. These cases were then scrutinized alongside daily meteorological parameters spanning from 2015 to 2019. To create forecasts for the occurrence of these extreme cases one day in advance, three distinct methods were employed: Logistic regression, Random Forest for Time Series, and Croston's method. RESULTS: For each of the analyzed stroke centers, the cross-correlations between instances of extremely high stroke numbers and meteorological factors yielded negligible results. Predictive performance achieved by forecasts generated through multivariate logistic regression and Random Forest for time series analysis, which incorporated meteorological data, was on par with that of Croston's method. Notably, Croston's method relies solely on the stroke time series data. All three forecasting methods exhibited limited predictive accuracy. CONCLUSIONS: The task of predicting days characterized by an exceptionally high number of strokes proved to be challenging across all three explored methods. The inclusion of meteorological parameters did not yield substantive improvements in forecasting accuracy.

Progress, challenges, and future perspectives of robot-assisted natural orifice specimen extraction surgery for colorectal cancer: a review.

With the continuous advancements in precision medicine and the relentless pursuit of minimally invasive techniques, Natural Orifice Specimen Extraction Surgery (NOSES) has emerged. Compared to traditional surgical methods, NOSES better embodies the principles of minimally invasive surgery, making scar-free operations possible. In recent years, with the progress of science and technology, Robot-Assisted Laparoscopic Surgery has been widely applied in the treatment of colorectal cancer. Robotic surgical systems, with their clear surgical view and high operational precision, have shown significant advantages in the treatment process. To further improve the therapeutic outcomes for colorectal cancer patients, some scholars have attempted to combine robotic technology with NOSES. However, like traditional open surgery or laparoscopic surgery, the use of the robotic platform presents both advantages and limitations. Therefore, this study reviews the current research status, progress, and controversies regarding Robot-Assisted Laparoscopic Natural Orifice Specimen Extraction Surgery for colorectal cancer, aiming to provide clinicians with more options in the diagnosis and treatment of colorectal cancer.

Trends of older gastric cancer incidence, mortality, and survival in the highest gastric cancer risk area in China: 2010-2019 and prediction to 2024.

BACKGROUND: Gastric cancer is a major health problem worldwide, with a high incidence among older adults. Given the aging overall population, it was crucial to understand the current burden and prospective trend of older gastric cancer. This study aimed to analyze the temporal trends of the incidence, mortality, and survival of older gastric cancer in the highest gastric cancer risk area in China from 2010 to 2019, and to predict the future burden of older gastric cancer up to 2024. METHODS: The study was conducted in Gansu province, an area characterized by the highest gastric cancer incidence and mortality in China. The registration data of gastric cancer incidence and mortality from 2010 to 2019 were pooled from registries in the Gansu Cancer Registration System, while survival data were collected from the First Hospital of Lanzhou University, Lanzhou University Second Hospital, and Gansu Cancer Hospital. Chinese standard population in 2000 and the Segi's world standard population were applied to calculate the age-standardized rate. Joinpoint regression was used to analyze the average annual percentage change (AAPC) in cancer incidence and mortality. Autoregressive Integrated Moving Average (ARIMA) models were employed to generate forecasts for incidence and mortality from 2020 to 2024. RESULTS: Based on registry data from 2010 to 2019, the incidence and mortality rates of gastric cancer among older adults remained stable. The incidence rates declined from 439.65 per 100,000 in 2010 to 330.40 per 100,000 in 2019, with an AAPC of -2.59% (95% confidence interval[CI], -5.14 to 0.04, P = 0.06). Similarly, the mortality rate changed from 366.98 per 100,000 in 2010 to 262.03 per 100,000 in 2019, with an AAPC of -2.55% (95% CI, -8.77-4.08%, P = 0.44). In the hospital-based cohort, the decline in survival rates was reported among older patients with gastric cancer in the highest gastric cancer risk area in China, with the 3-year overall survival (OS) decreasing from 58.5% (95% CI, 53.5-63.2%) in 2010 to 34.4% (95%CI, 32.1-36.7%) in 2019, and the 3-year progression-free survival (PFS) decreasing from 51.3% (95%CI, 47.5-55.1%) in 2010 to 34.2% (95%CI, 32.0-36.3%) in 2019, respectively. Moreover, forecasts generated by ARIMA models revealed a significant decline in the incidence and mortality of older gastric cancer in China from 2020 to 2024. Specifically, the incidence rate of older gastric cancer was expected to decrease from 317.94 per 100,000 population in 2020 to 205.59 per 100,000 population in 2024, while the anticipated mortality rate was estimated to decrease from 222.52 per 100,000 population in 2020 to 186.22 per 100,000 population in 2024. CONCLUSION: From 2010 to 2019, the incidence and mortality of older gastric cancer remained stable in the highest gastric cancer risk area in China, while the survival rates showed a decline. Based on the ARIMA models, it was anticipated that there might be a continued decline in older gastric cancer incidence and mortality in the highest-risk area in China over the next five years.

A Bayesian Projection of the Total Fertility Rate of Puerto Rico: 2020-2050.

OBJECTIVE: The abrupt decline in the Total Fertility Rate (TFR) of Puerto Rico to 0.9 children per woman, well below the replacement level of 2.1 children per woman, makes the prospect of a sustained population decline a real possibility. Population projections produced by the United States Census Bureau and the United Nations Population Division show that the island population may decline from 3.8 millions in 2000 to slightly above 2 million by 2050, a dramatic population decline of 47% in 50 years. Both population projections assume that all countries with a TFR below replacement level could eventually increase toward or oscillate to 2.1 children per woman and have Puerto Rico's TFR approaching 1.5 by 2050. This assumption has been widely criticized as unrealistic and not supported by evidence. The main objective of our research is to provide an alternative fertility projection for Puerto Rico by 2050 that has more realistic assumptions. METHODS: Our methodology is based on the Bayesian Hierarchical Probabilistic Theory used by the United Nations to incorporate a way to measure the uncertainty and to estimate the projection parameters. We modified the assumptions used by the United Nations by considering 17 countries with TFR similar to Puerto Rico. RESULTS: By 2050, Puerto Rico may have a TFR of 1.1 bounded by a 95% credibility interval (0.56,1.77). CONCLUSION: Under this scenario Puerto Rico can expect to have a larger population decline than that projected by the Census Bureau and the United Nations.