Misusing I2 for inconsistency, overlooking OIS for imprecision, and ignoring the continuum of certainty ratings: common pitfalls in GRADE assessments.

Unmodeled DIF in latent class analysis: Consequences for class enumeration

The intracluster correlation coefficient (ICC) is a critical parameter for determining sample size in cluster-randomized trials. However, accurate estimation of the ICC values are often challenging due to limited availability from participating sites. Even small deviations from the assumed ICC can result in underpowered studies. We used simulations to evaluate the performance of blinded internal pilot sample size re-estimation (SSRE) on the B-Free trial, a cluster crossover trial with 20 hospitals and 12 planned 4-weeks periods with postoperative delirium as primary outcome. The SSRE process included: (1) calculating the initial sample size, (2) collecting interim data, (3) estimating the ICC, (4) re-estimating the sample size, and (5) increasing the number of cluster-periods if needed. Across 1,000 simulations, interim ICC estimates closely matched true values when 25%, 50%, and 75% of the data were available, although variability was higher at 25%. Statistical power was restored to 80% and the Type I error was maintained at 5% when uncertainty in the ICC was modest (e.g., assumed 0.02 vs. observed 0.05). In the B-Free trial, the observed ICC at approximately 70% data availability closely approximated the final value. To achieve 81% power, the trial was extended by 5-6 periods at half of the participating sites. With at least 50% of interim data, ICC estimates are generally reliable, and SSRE preserves Type I error rate. Incorporating an internal pilot into the design of cluster crossover trials is an effective strategy to maintain statistical power when ICCs are modestly misspecified.

Treatment outcome and its predictors among patients with status epilepticus in Africa: A systematic review and meta-analysis.

Simulation and empirical evaluation of biologically-informed neural network performance

Abstract The psychological impact of connection to nature on pro-environmental behavior and individual well-being is a crucial factor in environmental psychology. While animals are often perceived as central elements of nature, the role of animal encounters in fostering connection to nature has not yet been systematically analyzed. This study investigates the empirical relationship between animals and connection to nature. Following the PRISMA 2020 guidelines, we searched four academic databases (ProQuest, Web of Science, ERIC, and PubMed) for empirical, peer-reviewed studies published between 2000 and 2024. A total of 116 studies met the inclusion criteria for the systematic review. These were analyzed for temporal trends, geographic origin, taxonomic focus, and sample size. Of these, 15 studies (yielding 23 independent samples) were eligible for meta-analysis. Most reviewed studies were conducted in North America, Europe, and Australia. Mammals and birds were the most frequently studied taxa, while amphibians, reptiles, and fish were underrepresented. Most studies had sample sizes below 1000 participants. The meta-analysis indicated a modest yet statistically significant positive effect of animal encounters on connection to nature (d = 0.18, 95% CI [0.10, 0.26]). The presence of publication bias was found to be negligible, and the heterogeneity among studies was low. These findings suggest that encounters with animals can contribute to the strengthening of connection to nature. Future research should investigate underrepresented animal groups and disentangle the specific contribution of animal interactions within interventions. • 116 studies on animals and humans' connection to nature were analyzed. • Mammals and birds are overrepresented; reptiles and fish are underrepresented. • A Meta-analysis based on 23 independent samples from 16 studies was conducted. • Animal contact has a small but significant positive effect ( d = 0.179). • Findings support using animal contact in conservation engagement efforts.

On the implementation of maximum entropy sampling with unequal probabilities and without replacement.

Artificial intelligence literacy in higher education and implications for nursing education: A scoping review.

Burn injuries in patients with epilepsy: A narrative review.

The burden of patient harm from unsafe care is disproportionately high in low-income countries, particularly in intensive care units (ICUs). Despite this, there is limited empirical evidence explaining the underlying challenges that make patient safety difficult to achieve in resource-limited ICUs. To explore the barriers to patient safety and incident-reporting in ICUs at Ethiopian university hospital. An exploratory qualitative descriptive study was conducted using in-depth interviews guided by a semi-structured protocol and informed by the Theoretical Domains Framework (TDF). Participants were purposively selected, and sample size was determined based on principles of data sufficiency. Reflexive thematic analysis with inductive orientation was employed following Braun and Clarke's approach. Thirty-nine healthcare professionals participated: 23 (59%) nurses, 13 (33%) physicians, and 3 (8%) clinical pharmacists. The mean age of participants was 32±5 years. Three overarching themes captured the barriers to patient safety and incident reporting in ICUs: (i) Fragile systems: Governance failure and resource constraints; (ii) Normalisation of deviance: teaching-hospital dynamics and clinical oversight, normalised communication lapses, and weak teamwork and relational context; and (iii) Silence trap: Fear in punitive and blame-oriented culture and lack of incident-reporting systems. Patient safety in Ethiopian ICUs is undermined by systemic, operational, and clinician-related obstacles. Structural and cultural barriers further hinder incident-reporting creating a vicious cycle where unsafe care remains hidden and opportunities for learning and improvements are lost. Breaking this cycle requires strong governance, coordinated resource investment, enhancing just culture, strengthening teamwork, establishing robust incident-reporting systems, and ongoing professional development focused on patient safety. Identifying barriers to patient safety and incident reporting in ICUs may enables targeted interventions that reduce preventable harm, improve care quality, and promote incident reporting and learning to sustain safe clinical practice.

Pathogenic Escherichia coli, Salmonella spp. and Campylobacter spp. are bacteria associated with foodborne diseases. This systematic review and meta-analysis investigates the prevalence of these pathogens in foods sold across seven East African Community (EAC) countries and identifies key risk factors for contamination. A comprehensive search for peer-reviewed papers and grey literature was conducted in six databases (PubMed, CAB Direct, African Journals Online, Google Scholar, ScienceDirect, and Web of Science), as well as 12 online repositories. The review encompassed studies published in English and French between January 2000 and June 2022, adhering to the 2020 guidelines for the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Eligible studies employed probabilistic sampling and reported the proportion of contaminated samples. Out of 4134 initial records, 53 studies met the inclusion criteria. Most were conducted in Kenya (n=22) and Tanzania (n=21), with no eligible studies found for Burundi and South Sudan. E. coli and Salmonella spp. were the most frequently studied pathogens, while Campylobacter spp. was less represented. Using a random-effects model in Stata®, pooled prevalence estimates were 41% for E. coli (95% CI: 34-52%), 12% for Salmonella spp. (95% CI: 12-27%), and 9% for Campylobacter spp. (95% CI: 7-32%). Significant heterogeneity was observed and further explored through meta-regression and subgroup analyses. Contamination levels varied by food type, processing status, sample size, and country. Meat, especially poultry, showed the highest prevalence of bacterial contamination across all pathogens. Alarmingly, beverages were also highly contaminated, with E. coli detected in 66.3% (95% CI: 31-89%) and Salmonella spp. in 11.8% (95% CI: 1-55%) of samples. Key risk factors included poor hygiene practices, inadequate sanitation, high storage temperatures, and a lack of food safety training. These findings underscore the urgent need for improved food safety measures in the EAC region for improved public health and support trade advancement. The study also highlights critical gaps in surveillance, particularly for Campylobacter spp., pathogenic E. coli, and data from some EAC countries.

To evaluate exvivo accuracy of robotic computer-assisted implant surgery (r-CAIS) for quad zygomatic implant (ZI) placement with a novel semi-autonomous, optically-driven, haptic-assisted system. Two expert operators placed ZIs in human cadavers. Accuracy was evaluated by superimposing pre- and post-operative cone-beam computed tomography (CBCT) scans, measuring linear (mm) on the x-, y-, and z- axes and angular (degrees) deviations. Sample size was calculated. The main analysis focused on composite outcomes (global platform, global apical, and angular deviation), while extended analyses examined deviations on individual spatial axes (buccal-lingual, mesial-distal, depth, and non-depth) for both implant platform and apex. Three multivariate models were executed on global platform, global apical, and angular deviation considering implant position (anterior vs. posterior) and quadrant (left vs. right). Forty ZIs were placed in 10 human cadavers. The mean global deviations at implant platform and apex were 1.16 mm (standard deviation (SD) 0.47) and 1.51 mm (SD 0.70), with mean angular deviation of 1.04° (SD 0.65°). In the multivariate models, implant position was found to be significant for global platform deviation (p = 0.0218), with posterior implants performing better, and for angular deviation (p = 0.0465), with anterior implants performing better. The optically driven haptic-assisted robotic system demonstrated high accuracy in quad ZI surgery, with all implants placed without intraoperative complications or significant errors impacting adjacent anatomical structures. Mean global linear deviations of 1 and 1.5 mm at platform and apex, and 1° of angular deviation were experienced. Further research is necessary to validate these promising results invivo.

Plastic pollution is increasing in our oceans, and while exposure is also increasing in many wild species, there are exceptions. Understanding which species are at risk is therefore important to identifying conservation priorities and responding to the pollution crisis. Here, we document ingested plastics in Kermadec Petrels (Pterodroma neglecta) on uninhabited Henderson Island and Oeno Atoll in the remote South Pacific and compare our results with historical data. While sample sizes are limited, the data suggests plastic ingestion has remained consistent over >30years, with 23.1% of fledglings (n=13) sampled in 2025 containing small quantities of plastics (mean±SD: 0.0113±0.0061g, median pieces 2.0). These data contrast with plastic ingestion records for other closely related species in the region, suggesting Kermadec Petrels can selectively avoid consuming plastic, or forage in locations with lower plastic densities, and are therefore at less risk of exposure.

Although sample size calculation for open-cohort longitudinal cluster randomized trials (LCRTs) under a fixed design framework was developed by Kasza etal., unifying the closed-cohort and repeated cross-sectional sampling provided in Hooper etal. when a churn rate is constant, there has been no prior efforts in developing optimal open-cohort LCRTs that maximizes the design efficiency. This work assumes a prespecified number of periods and a constant number of replaced individuals at each period in open-cohort LCRTs. We propose algorithms for deriving optimal sample size under a cost-efficiency framework and arrive at the local optimal design (LOD) for fixed correlation parameters and MaxiMin optimal design for addressing uncertainty in correlation parameters. When correlation parameters are known, as the number of replaced individuals increases, for open-cohort PA-LCRTs, the optimal cluster-period size generally decreases and then increases whereas the optimal number of clusters and power under LOD first increase and then decrease. In contrast, for CRXO trials and standard SW-CRTs, the optimal cluster-period size and churn rate under LOD increase whereas the optimal number of clusters and power under LOD decrease. When correlation parameters are unknown, but the parameter space is available, with a small number of replaced individuals, there is no difference in optimal designs between PA-LCRTs and CRXO trials. The number of replaced individuals also has less impact on the optimal cluster-period size than optimal number of clusters. We demonstrate our new optimal design methods using the context of two real-world LCRTs.

Risk of total knee replacement after a meniscal tear treated with arthroscopic partial meniscectomy compared to nonoperative treatment: A systematic review.

Leveraging real-world data (RWD) from dental records offers significant potential to overcome logistical and financial barriers in population-based caries research; however, comprehensive evaluations of such studies remain limited. This meta-epidemiological study systematically characterized the temporal and geographical distribution, study characteristics, and methodological quality of RWD-based caries research. A systematic literature search across MEDLINE, EMBASE, and Web of Science databases through December 2024 identified 230 eligible studies. Publication increased exponentially, with 57.8% published between 2015 and 2024, predominantly from Nordic countries (38.3%) and the United States (22.0%). Three-quarters were published in dental journals. About half used a cohort study design. Most studies (80.0%) used dental records from various sources. Over 25% had a sample size exceeding 10,000. Nordic studies frequently used public/school dental service records and, with larger samples, whereas U.S. studies primarily used university dental school records and focused more on adults and older persons. More association-based studies were published after 2015, with larger sample sizes. Nevertheless, 47.4% of studies inadequately reported caries examination methods, and only 8.7% reported agreement test results. Although use of multivariable regression for confounding adjustment increased markedly after 2015 (38.6%-60.5%), causal inference frameworks and advanced analytic methods for confounding adjustment remained rare (4.4%) even among studies investigating causal relationships, and only 7.0% addressed missing data. RWD-based caries research has grown rapidly, but with notable methodological limitations, highlighting the urgent need for enhanced reporting standards, analytical rigor, and practical strategies to improve quality across diverse clinical settings and resource contexts.

Narrative review of screening and assessment of cognitive-communication disorders in Moroccan Amazigh (Berber) speakers.

To analyze the association between myelodysplastic syndrome (MDS)/acute myeloid leukemia (AML) and PARP inhibitors (PARPi), to explore predisposing factors, and to evaluate their effectiveness and safety in clinical practice. Observational, analytical and retrospective study including patients with ovarian cancer treated with olaparib or niraparib from 2014 to 2024. Primary endpoint was to identify the incidence and risk factors of secondary malignancy, considering treatment duration, BRCA mutation, lines of platinum-based chemotherapy and grade 3-4 adverse events (AEs). Overall survival (OS), progression-free survival (PFS), and AEs were recorded. Eighty patients were included. Six developed secondary MDS/AML, with an incidence of 11.6% (5/43) for olaparib and 2.7% (1/37) for niraparib. No significant correlation was found, except for hematological grade 3-4 AEs, associated with a higher risk of developing MDS/AML (OR: 32.3 p = 0.001). The median latency period was 8.8months (1.5-102.2). For olaparib, median PFS was 37.8months (95% CI 17.0-58.6); OS was not reached. For niraparib PFS was 10.0months (95% CI 7.4-12.5), OS 25.1months (95% CI 6.3-43.9). Grade 3-4 AEs occurred in 32.6% of olaparib and 35.2% of niraparib patients, mainly anemia and thrombocytopenia. PARPi are effective and generally with a manageable safety profile. However, secondary MDS and AML, though rare, remain serious adverse events with incompletely defined risk factors. The observed incidence, particularly with olaparib, is comparable to or higher than that reported in trials, though the sample size was limited. Close monitoring of hematologic AEs may be key to preventing these neoplasms.

Effectiveness of transcranial direct current stimulation (tDCS) on attention and memory biases in individuals with post-traumatic stress disorder (PTSD): a randomized, single-blind, parallel-group study.

• Benchmarked tabular data into synthetic image methods for regression and classification on diverse datasets. • Analyzed computational trade-offs and scalability, guiding applicability to large-scale tabular data. • Showed hybrid models (CNN+MLP, ViT+MLP) consistently improve performance with synthetic images. • Performed critical analysis of key limitations in current tabular data into synthetic image methods. Despite the success of deep neural networks on perceptual data, their performance on tabular data remains limited, where traditional models still outperform them. A promising alternative is to transform tabular data into synthetic images, enabling the use of vision architectures such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). However, the literature lacks a large-scale, standardized benchmark evaluating these transformation techniques. This work presents the first comprehensive evaluation of nine spatial encoding methods across 24 diverse regression and classification datasets. We assess performance, scalability, and computational trade-offs under a unified framework with rigorous hyperparameter optimization. Our results reveal a performance landscape structured by data regimes, defined by sample size ( N ) and dimensionality ( d ), and show that the transformation method exerts a significantly stronger influence on predictive performance than the chosen vision architecture. In particular, REFINED emerges as the most robust transformation across tasks and datasets. Hybrid models (CNN+MLP, ViT+MLP) consistently reduce predictive variance, offering advantages especially in smaller datasets, yet play a secondary role. These findings suggest that transforming tabular data into synthetic images is a powerful, yet data-dependent, strategy. This benchmark provides clear guidance for researchers and practitioners, offering key insights into scalability, transformation behavior, and architectural interplay, establishing a comprehensive reference for future research on spatial encodings for tabular data.