THE CORRELATION BETWEEN ORAL AND GENERAL HEALTH-RELATED QUALITY OF LIFE IN ADULTS: A SYSTEMATIC REVIEW AND META-ANALYSIS.

Objective: The relationship between general health and oral health is critical for understanding the broader implications of oral health on overall well-being and vice versa. The impact of oral and general health on individuals can be comprehensively captured by the concepts oral and general health-related quality of life (OHRQoL and HRQoL), respectively. This systematic review and meta-analysis aimed to synthesize existing evidence on the correlation between OHRQoL and HRQoL across different adult populations.

Time delay effects on collective resonant behaviors in two coupled fractional oscillators with mass fluctuations.

In this study, we introduce a coupled fractional system consisting of two fluctuating-mass oscillators with time delay and investigate their collective resonant behaviors. First, we achieve complete synchronization between the average behaviors of these oscillators. We then derive the exact analytical expression for the output amplitude gain, and based on this, we observe generalized stochastic resonance (GSR) in the system.

The Role of Double-Zero-Event Studies in Evidence Synthesis: Evaluating Robustness Using the Fragility Index.

Rationale: Zero-event counts are common in clinical studies, particularly when assessing rare adverse events. These occurrences can result from low event rates, short follow-up periods, and small sample sizes. When both intervention and control groups report zero events in a clinical trial, the study is referred to as a double-zero-event study, which presents methodological challenges for evidence synthesis.

Effects of double data extraction on errors in evidence synthesis: a crossover, multicenter, investigator-blinded, randomized controlled trial.

Objectives: The objective was to investigate the role of double extraction in reducing data errors in evidence synthesis for pharmaceutical and non-pharmaceutical interventions.

Design: Crossover randomized controlled trial (RCT).

Setting: University and hospital with teaching programs in evidence-based medicine.

Confidence intervals of the relative risk and odds ratio can predict when the optimal information size in a meta-analysis is not met.

Objectives: We sought to empirically evaluate whether the width of confidence interval (CI) of the relative risk (RR) and odds ratio (OR) can obviate the need for calculating the optimal information size (OIS) when making Grading of Recommendations Assessment, Development and Evaluation imprecision judgments.

Study Design And Setting: We analyzed a convenience sample of meta-analyses extracted from the Cochrane Database of Systematic Reviews. From each meta-analysis, we calculated OIS based on relative risk reductions (RRRs) of 15%-50% and evaluated the ratio of upper to lower 95% CI boundaries of RR (RR CI ratio) and OR (OR CI ratio).

Trial sequential analysis involving same-year studies requires careful temporal ordering.

Trial sequential analysis (TSA) is an increasingly used tool in systematic reviews to monitor synthesized evidence. However, the current practice of TSAs often overlooks the order of same-year studies, which are typically ordered alphabetically based on the last names of the studies' authors by default in the widely used TSA software application. This practice is inappropriate and contrary to the TSA's definition.

Choice of Link Functions for Generalized Linear Mixed Models in Meta-Analyses of Proportions.

Two-step approaches for synthesizing proportions in a meta-analysis require first transforming the proportions to a scale where their distribution across studies can be approximated by a normal distribution. Commonly used transformations include the log, logit, arcsine, and Freeman-Tukey double-arcsine transformations. Alternatively, a generalized linear mixed model (GLMM) can be fit directly on the data using the exact binomial likelihood.

Non-parametric Bayesian approach to multiple treatment comparisons in network meta-analysis with application to comparisons of anti-depressants.

Network meta-analysis is a powerful tool to synthesize evidence from independent studies and compare multiple treatments simultaneously. A critical task of performing a network meta-analysis is to offer ranks of all available treatment options for a specific disease outcome. Frequently, the estimated treatment rankings are accompanied by a large amount of uncertainty, suffer from multiplicity issues, and rarely permit possible ties of treatments with similar performance.

Comparison of responders and nonresponders with knee osteoarthritis after transcranial direct current stimulation.

Aim: The study compared responders and nonresponders to transcranial direct current stimulation (tDCS) regarding clinical pain outcomes in knee osteoarthritis (OA) patients.

Patients And Methods/materials: Sixty participants received home-based active tDCS, and clinical pain outcomes were compared between responders and nonresponders.

Results: Latent class growth analyses classified 41 participants as responders and 19 as nonresponders.

The joint associations of ambient air pollutants and weather factors with mortality: Evidence from a national time-stratified case-crossover study in China.

Background: People in daily life are usually exposed to multiple environmental factors, but few studies have evaluated the joint health impacts of ambient air pollutants and weather factors.

Objectives: To investigate the joint associations of short-term exposures to ambient air pollutants and weather factors with mortality and estimate the mortality burden attributable to these multiple environmental exposures in China.

Methods: We collected individual death information from six provinces (Guangdong, Yunnan, Hunan, Zhejiang, Tibet and Jilin) in China during 2013 to 2018, and applied a time-stratified case-crossover study design to estimate the joint associations of air pollutants [PM (particulate matter with an aerodynamic diameter ≤2.

Robustness Assessment of Oncology Dose-Finding Trials Using the Modified Fragility Index.

Objectives: The sample sizes of phase I trials are typically small; some designs may lead to inaccurate estimation of the maximum tolerated dose (MTD). The objective of this study was to propose a metric assessing whether the MTD decision is sensitive to enrolling a few additional subjects in a phase I dose-finding trial.

Methods: Numerous model-based and model-assisted designs have been proposed to improve the efficiency and accuracy of finding the MTD.

Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study.

Background: From January 2020 to June 2022, strict interventions against COVID-19 were implemented in Guangdong Province, China. However, the evolution of COVID-19 dynamics remained unclear in this period.

Objectives: This study aims to investigate the evolution of within- and between-city COVID-19 dynamics in Guangdong, specifically during the implementation of rigorous prevention and control measures.

Towards the automatic risk of bias assessment on randomized controlled trials: A comparison of RobotReviewer and humans.

RobotReviewer is a tool for automatically assessing the risk of bias in randomized controlled trials, but there is limited evidence of its reliability. We evaluated the agreement between RobotReviewer and humans regarding the risk of bias assessment based on 1955 randomized controlled trials. The risk of bias in these trials was assessed via two different approaches: (1) manually by human reviewers, and (2) automatically by the RobotReviewer.

Simultaneous evaluation of the imprecision and inconsistency domains of GRADE can be performed using prediction intervals.

Objectives: To explore the use of prediction interval (PI) for the simultaneous evaluation of the imprecision and inconsistency domains of Grading of Recommendations, Assessment, and Evaluation using stakeholder-provided decision thresholds.

Study Design And Setting: We propose transforming the PI of a meta-analysis from a relative risk scale to an absolute risk difference using an appropriate baseline risk. The transformed PI is compared to stakeholder-provided thresholds on an absolute scale.

Benchmarking DNA Foundation Models for Genomic Sequence Classification.

The rapid advancement of DNA foundation language models has revolutionized the field of genomics, enabling the decoding of complex patterns and regulatory mechanisms within DNA sequences. However, the current evaluation of these models often relies on fine-tuning and limited datasets, which introduces biases and limits the assessment of their true potential. Here, we present a benchmarking study of three recent DNA foundation language models, including DNABERT-2, Nucleotide Transformer version-2 (NT-v2), and HyenaDNA, focusing on the quality of their zero-shot embeddings across a diverse range of genomic tasks and species through analyses of 57 real datasets.

ZIBGLMM: Zero-Inflated Bivariate Generalized Linear Mixed Model for Meta-Analysis with Double-Zero-Event Studies.

Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis. Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc methods can yield biased conclusions. Although the standard bivariate generalized linear mixed model can accommodate DZS, it fails to address the potential systemic differences between DZS and other studies.

Methods for assessing inverse publication bias of adverse events.

In medical research, publication bias (PB) poses great challenges to the conclusions from systematic reviews and meta-analyses. The majority of efforts in methodological research related to classic PB have focused on examining the potential suppression of studies reporting effects close to the null or statistically non-significant results. Such suppression is common, particularly when the study outcome concerns the effectiveness of a new intervention.

Accelerating evidence synthesis for safety assessment through ClinicalTrials.gov platform: a feasibility study.

Background: Standard systematic review can be labor-intensive and time-consuming meaning that it can be difficult to provide timely evidence when there is an urgent public health emergency such as a pandemic. The ClinicalTrials.gov provides a promising way to accelerate evidence production.

Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis.

Systematic reviews and meta-analyses are essential tools in contemporary evidence-based medicine, synthesizing evidence from various sources to better inform clinical decision-making. However, the conclusions from different meta-analyses on the same topic can be discrepant, which has raised concerns about their reliability. One reason is that the result of a meta-analysis is sensitive to factors such as study inclusion/exclusion criteria and model assumptions.