Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 9 appraisals
European journal of trauma and emergency surgery : official publication of the European Trauma Society
Pattern of blast injuries. A systematic review: Part 2 - Landmines, unexploded ordnance and terrorism.
PURPOSE: This systematic review analyzes the patterns and distribution of blast injuries from landmine explosions, UXOs and terror attacks including suicide bombing, in civilians and military personnel. METHODS: A search was conducted in two databases on 26. March 2025 for observational studies reporting blast injuries in civilian and military populations of all ages. Studies were clustered by explosion typ. A narrative data synthesis was performed. The JBI tool for case series was used. PROSPERO: CRD42023391736. RESULTS: 126 studies were included in the overall systematic review. 45 studies on terror & suicide bombings and 12 studies on landmines & UXOs are analyzed in the present paper. Studies on landmines from 1980 to 2006 included 13,270 individuals (age: 24.0 years (Range 8.2 years - 32.2 years); mainly male (88.9%)) in post-conflict low- and middle-income countries. Lower extremity injuries including amputations were most frequent, followed by eye injuries. Studies on terrorist attacks with a total of 12,518 mainly civilian victims (average age: 33,1 years; 61.6% males) reported on incidents in post-conflict regions and high-income cities mainly from the early 2000s. The injury pattern was multidimensional, with extremity injuries most frequent (28%-30%), including fractures, lacerations, and traumatic amputations (18%-20%). CONCLUSION: This Systematic Review Part 2 on landmines and terror attacks found that extremity injuries, including traumatic amputations, are the most frequent and dominate injury. However, the injury pattern varies depending on the explosion mechanism. Landmines primarily cause lower extremity injuries, as well as eye injuries. Terror and suicide explosions exhibit multidimensional injury pattern depending on the distance and location of the explosion.
19 June 2026
Read appraisal →PloS one
Empowering rural governance with digital technology: Deep learning models for automated detection of rural buildings using remote sensing images
Building detection from drone imagery represents a transformative approach to rural governance by enabling precise spatial data acquisition for critical applications including illegal construction monitoring, disaster assessment, and cadastral mapping. However, automated detection systems face persistent challenges including extreme scale variations in rural buildings, complex background interference from vegetation and shadows leading to boundary ambiguity, and severe scarcity of high-quality annotated datasets that limit model generalization. To overcome these limitations, this study introduces an integrated framework featuring three innovative components: the Multi-scale Hybrid Attention module employs parallel convolutional pathways with channel and spatial attention to dynamically capture multi-scale features while suppressing background noise; the Dynamic Feature Pyramid Network utilizes content-aware routing to adaptively fuse hierarchical features for optimal scale-invariant representation; and the Progressive Contrastive Learning strategy leverages both labeled and unlabeled data through hard sample mining to enhance discriminability under data constraints. Extensive experiments validate the model's efficacy, achieving a mean Intersection over Union (MIoU) of 87.3%, pixel accuracy (PA) of 94.2%, and mean Average Precision (mAP) of 89.6% on the Massachusetts Buildings Dataset, substantially surpassing benchmarks like U-Net (80.1% MIoU), SegNet (78.9% MIoU), and DeepLabV3+ (82.4% MIoU), with ablation studies confirming critical module contributions (e.g., MIoU drops to 81.5% without MHA). The framework demonstrates robust cross-dataset generalization (72.3% MIoU on Chinese rural data) and effective problem resolution, establishing a scalable solution for intelligent rural governance through accurate building extraction. The dataset and code used in this study have been uploaded to the GitHub website: https://github.com/xiexie1234567890/rural_building_detection/tree/main.
14 June 2026
Read appraisal →BMJ global health
Systematic review and meta-analysis of interventions for mental health in extreme weather events
INTRODUCTION: Climate change leading to extreme weather events (EWEs) poses critical threat to human health, including mental well-being. As the effects of climate change are becoming more pronounced, there is a need to identify effective interventions to mitigate the mental health impacts. METHODS: The aim of this review was to synthesise the existing literature on interventions to prevent and manage mental health symptoms in people affected by EWEs. We conducted a comprehensive literature search in MEDLINE, EBSCO CINAHL and Cochrane till 7 February 2024 and in Web of Science and EMBASE till 20 February 2024. Randomised controlled trials (RCTs), quasi-experimental studies and before-after study designs were included. Our search yielded 11 083 potentially relevant papers, which were screened by titles and full texts and 33 studies were finally included in the review. We recorded the study, participants, intervention, disaster and other relevant data on Excel in duplicate. We conducted a meta-analysis separately for intervention-control and before-after studies using Review Manager 5.4. The Cochrane Risk of Bias tool II was used to assess risk of bias in RCTs and the risk of bias in non-randomised studies of interventions tool for quasi-experimental and before-after designs. Main outcomes were symptoms of post-traumatic stress disorder (PTSD), depression, anxiety, stress, general functioning and well-being. RESULTS: There was significant improvement in mental health symptoms following implementation of various interventions, leading to a decrease in PTSD symptoms (standardised mean difference (SMD) -2.92, 95% CI (-4.53 to -1.31), six intervention-control studies and SMD -0.71, 95% CI (-0.95 to -0.48), five before-after studies), depression symptoms (SMD -0.28, 95% CI (-0.53 to -0.03), three intervention-control studies and SMD -1.00, 95% CI (-1.41 to -0.59), 11 before-after studies), anxiety symptoms (SMD -0.38, 95% CI (-0.69 to -0.06), two studies and SMD -0.35, 95% CI (-0.47 to -0.23), eight before-after studies), stress symptoms (SMD -0.82, 95% CI (-1.10 to -0.53), two before-after studies), general functioning impairment (SMD -1.22, 95% CI (-1.73 to -0.70), three before-after studies) and an improvement in well-being (SMD 0.29, 95% CI (0.15 to 0.42), three intervention-control studies and SMD 0.67, 95% CI (0.25 to 1.09), two before-after studies). CONCLUSION: There are existing interventions to improve mental health after an EWE, and these should be integrated into all relevant disaster management plans.
30 May 2026
Read appraisal →Global health action
Integrating GIS and local knowledge for optimised medical supply prepositioning in flood-prone Nigeria: a strategic framework
BACKGROUND: Nigeria faces recurring flood disasters that severely disrupt its healthcare systems and delays medical relief efforts. Current medical supply prepositioning by the National Emergency Management Agency (NEMA) remains inadequate due to infrastructure limitations, funding constraints, and poor coordination. OBJECTIVE: To develop and validate a Strategic Medical Hub Locator Framework that integrates Geographic Information Systems (GIS) analysis with qualitative local knowledge to optimise medical supply prepositioning for flood emergencies in Nigeria. METHODS: We employed a mixed-methods approach, combining qualitative analysis of 22 GIS-based flood risk studies in Nigeria and structured expert survey responses from 34 professionals with flood response experience, including five with direct NEMA warehouse experience. The framework was grounded in Disaster Risk Reduction theory and supported by Protection Motivation Theory and Theory of Planned Behaviour. RESULTS: The framework identified eight strategic medical hub locations: six existing NEMA warehouses (Abuja, Lagos, Kano, Gombe, Maiduguri, Kaduna) with the strongest performance, and two new strategic sites (Akure and Uyo) to address service gaps in South-South and South-East regions. Abuja emerged as the highest-performing hub with a composite score reflecting good centrality, governance capacity, and operational readiness. The framework successfully bridged data silos between spatial flood risk patterns and operational constraints. CONCLUSIONS: Integration of GIS thematic analysis with qualitative local data provides a viable, replicable method for enhancing humanitarian medical logistics in flood-prone regions. The Strategic Medical Hub Locator Framework offers a structured decision-support tool that can inform evidence-based investment in Nigeria's disaster health infrastructure, while being adaptable to other Global South contexts. Key findings: Where primary geospatial data silos are present, GIS analysis can be enhanced by combining it with local qualitative insights, thereby bridging the gap between technical modelling and practitioner realities.Added knowledge: The paper offers a methodological contribution through the design of a Strategic Medical Hub Locator Framework that is replicable in contexts where primary data and complex modelling are not feasible.Global health impact for policy and action: This study provides policymakers and humanitarian actors with a proof-of-concept decision-support tool. The exploratory framework offers a scalable model that can be adapted to other flood-prone settings where primary data and practical knowledge are scattered, thereby strengthening disaster preparedness and health system resilience strategies.
27 May 2026
Read appraisal →Disaster medicine and public health preparedness
Hospital Medical Surge Capacity Assessment and Planning Tools for Disaster Response: A Systematic Review and Meta-analysis
BACKGROUND: Hospital surge capacity is pivotal for disaster response, yet the effectiveness of assessment and planning tools remains underexplored. OBJECTIVE: This systematic review evaluates the reliability, validity, and applicability of surge capacity tools across disaster scenarios. METHODS: Following PRISMA guidelines, we analyzed 24 Q1 studies (PubMed, Scopus) using the PICO framework. Risk of bias was assessed via ROBINS-E, and meta-analysis (random-effects model) quantified pooled effect sizes (ES). Subgroup analyses compared simulation tools, surveys, and resource strategies. RESULTS: The combined ES was 0.74 (95% CI: 0.67-0.82), indicating moderate improvement in preparedness. Simulation tools (Group A) showed the highest consistency (ES = 0.81, I2 = 0%), while resource strategies (Group C) had wider variability (ES = 0.71, I2 = 74.42%). Egger's regression revealed publication bias (P = 0.004), and heterogeneity was substantial (I2 = 76.70%). CONCLUSIONS: Simulation-based tools are most effective for surge capacity, whereas resource strategies require contextual adaptation. Policymakers should prioritize standardized benchmarks and DSS integration, with future research addressing equity and real-world implementation gaps.
20 May 2026
Read appraisal →Current psychiatry reports
The Type of Providers Delivering Practice Elements Used in Child Mass Trauma Interventions: A Systematic Review
PURPOSE OF REVIEW: This review identified the relative frequency of use of various types of providers delivering practice elements in 130 child mass trauma interventions and compared the use of these providers across events, populations, and settings. RECENT FINDINGS: Mental health professionals (MHPs) more frequently delivered cognitive, self-monitoring, exposure, and narrative elements than other providers while non-specialist providers (NSPs) and teachers and school personnel (TSPs) delivered support networking and social skills training more frequently. MHPs administered interventions to targeted populations more frequently, and TSPs administered interventions to universal populations more frequently. Only MHPs administered interventions at clinical facilities; NSPs delivered a majority of interventions at community sites. Understanding the specific practice elements delivered by providers with different backgrounds and training can inform efforts to enhance the reach and impact of mass trauma services. Future publications should report greater detail about providers and their training and about specific practice elements used.
14 May 2026
Read appraisal →PloS one
Research on interpretable machine learning modeling and spatial prediction of cross-landform landslide disaster mechanism: A case study of Shaanxi Province
The differential understanding of the landslidetriggering mechanisms across various geomorphic units is vital for enhancing regional disaster prevention. This study investigates the Loess Plateau area in northern Shaanxi and the Qinba Mountain Area in southern Shaanxi as the study areas, and constructs a 15dimensional evaluation factor system covering topography, geology, vegetation, and human activities. After eliminating collinearity factors via double tests of the Pearson correlation coefficient and variance inflation factor, Bayesian optimization is used to optimize hyperparameters for CatBoost, Random Forest, LightGBM, and XGBoost, and the SHAP framework is combined to perform global attribution, single-factor dependency analysis, and interaction-effect quantification. The results demonstrate that CatBoost performs best across both geomorphic areas, achieving AUCs of 0.8307 and 0.8252 in the test sets. In northern Shaanxi, a discrete patch pattern driven by "human-water" coupling is observed, with population density and the topographic moisture index contributing 37.2%. In contrast, Southern Shaanxi exhibits a continuous, band-shaped distribution controlled by "structure-topography" is observed, and elevation and lithology dominate the model's decision-making. The SHAP-dependent map identified cross-geomorphic differences in the population density threshold of 99.83 people/km2, the slope threshold of 2.21° in northern Shaanxi, and the elevation threshold of 1194.73m in southern Shaanxi. Furthermore, the interaction network revealed an antagonistic effect of DEM and POP in northern Shaanxi and of DEM and LULC in southern Shaanxi. The framework proposed a quantitative, explainable basis for differentiated disaster prevention strategies in this research.
11 May 2026
Read appraisal →PloS one
Optimizing LSTM networks and feature selection algorithms using GEE data
Feature selection uncertainty and suboptimal model configuration in flood susceptibility mapping (FSM) are critical challenges for disaster risk reduction. This study introduces a novel integrated framework that couples feature selection strategy with metaheuristic-optimized deep learning for high-precision FSM in the flood-prone Khuzestan Province, Iran. An initial set of 19 factors was sourced from Google Earth Engine (GEE). The most influential variables were identified using an ensemble of nine feature selection methods, including Boruta, Boruta-SHAP, Elastic-Net, Mutual Information, Permutation Importance, Recursive Feature Elimination (RFE), Sequential Forward Selection (SFS), Stability Selection, and Deep Feature Importance. For model development, 1,000 sample points were used, consisting of 500 randomly selected non-flood points (value 0) and 500 flood points (value 1), with the trained model subsequently generalized to the entire study area. In this process, a frequency-based consensus rule was applied, whereby variables were retained only if selected by a majority of methods. This process established the Normalized Difference Vegetation Index (NDVI) and Daily Minimum Temperature (TMMN) as the most critical predictors. A Long Short-Term Memory (LSTM) was developed using this optimal feature set and subsequently enhanced through hyperparameter optimization with five advanced metaheuristic algorithms, including WOA, GWO, OOA, CSA, and HOA. The model validation demonstrated that optimization significantly boosted performance, with the LSTM-WOA model emerging as superior, achieving the highest F1-Score (0.88) and Cohen's Kappa (0.75). The final FSM identified the northwestern and central regions as the highest susceptibility. The study innovation lies in its formalized consensus feature selection and comparative metaheuristic optimization, providing a reliable tool for FSM in arid and semi-arid regions.
3 May 2026
Read appraisal →Disaster medicine and public health preparedness
The Beirut Pager Explosions of 2024: A Systematic Review of an Unprecedented Mass Casualty Event
OBJECTIVE: In September 2024, Lebanon experienced an unprecedented mass casualty incident involving the simultaneous detonation of thousands of weaponized pager devices. This systematic review characterizes the resulting injury patterns, surgical burden, and disaster-response lessons to help inform future preparedness and response strategies. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies on casualties from the pager explosions. A random-effects meta-analysis estimated pooled prevalence of injuries by body region. RESULTS: Sixteen articles met inclusion criteria overall, comprising seven studies in the clinical synthesis and nine articles in the qualitative disaster-response synthesis. A consistent injury pattern predominantly affected the hands, face, and eyes. Specialty-specific cohorts demonstrated severe ocular destruction, frequent hand amputation, and a substantial operative burden. In the two comparable hospital-based cohorts eligible for meta-analysis, upper extremity injuries were most common (pooled prevalence 84%; 95% CI: 73-91%; I2 = 0%). The incident exposed critical weaknesses in triage, communication, and specialty surge capacity. CONCLUSION: The 2024 pager explosions produced a novel injury signature distinct from conventional blast trauma. These findings, while based on limited case series, underscore the urgent need for health care systems to develop flexible, adaptive disaster plans that can respond to the unique threats of unconventional warfare.
14 Apr 2026
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