Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Archives of toxicology
A review of machine learning in toxicology: current practices and reporting gaps
In recent years, machine learning and artificial intelligence approaches have been increasingly applied in the context of toxicological risk assessment. Many published overview, review, and comment papers discuss advantages, disadvantages, success stories, and open challenges for the application of machine learning models in toxicology. Machine learning methods using information from in vitro experiments can help to avoid animal experiments, thus allowing for larger numbers of experiments to be conducted. Drawbacks of machine learning models are the lack of mechanistic interpretability and the need for large amounts of high-quality data. In this work, we present a literature review of papers indexed in PubMed or published in the journal Computational Toxicology in the years 2022 to 2024, to assess the usage of machine learning methods in toxicology as well as the practices in reporting of methods and corresponding results. We do not address the suitability or the performance of methods, which is impossible to assess objectively without reanalysis on raw data, but focus on common practices and gaps in reporting. Major results are that many different machine learning methods are used in toxicology, often with appropriate internal validation. However, in only half of the cases, interpretation methods are used to address the problem that these models often make predictions as a black box. Moreover, there are very frequent gaps in reporting, in particular related to handling of missing values, and availability of data and code. Thus, this review can serve as a starting point for further tailored methodological research and guidance.
2 Aug 2026
Read appraisal →International journal of biometeorology
Scorpion sting stratification in Iran: A systematic review of epidemiological patterns, determinants, and climate change impacts
This systematic review provides a comprehensive synthesis of the epidemiology of scorpion sting stratification across Iran's geography, a critical public health concern in its arid and semi-arid regions. Conducted in accordance with PRISMA 2020 guidelines, the review analyzes the spatial and temporal patterns of envenomations, identifies key ecological, climatic, and anthropogenic determinants, and evaluates the growing evidence for climate change as a driver of increased risk. The study confirms hyperendemic levels of scorpion stings in the southwestern and western provinces, with species from the genera Hemiscorpius and Androctonus presenting the most severe medical threat. A meta-analysis of national data yields a pooled incidence rate of 92.7 per 100,000 population per year (95% CI: 68.4-117.0), with extreme heterogeneity (I² = 98.9%) reflecting stark regional disparities. The primary determinants of scorpion distribution and human-scorpion conflicts include climatic variables (temperature, precipitation, and humidity), land-use practices, unplanned urbanization, and occupational exposure. Recent evidence from species distribution modeling strongly suggests that climate change is expanding suitable habitats for scorpions, prolonging their active seasons, and increasing human-scorpion conflicts. This review highlights the critical need for improved surveillance, predictive spatial risk models, and climate-adaptive public health strategies to address this growing threat.
11 July 2026
Read appraisal →Psychological medicine
Extreme heat and mental health: systematic review and qualitative investigation of risk and protective factors
BACKGROUND: Extreme hot weather poses increasing risks to mental health. Yet, factors affecting vulnerability are under-researched. This mixed-method study integrates a systematic review and qualitative investigation to identify risk and protective factors for heat-related mental health issues, leading to the co-development of a screening tool. This could inform future research and, pending validation in clinical settings, support mental health professionals in assessing vulnerability among service users. METHODS: We searched PubMed and Web of Science for publications on extreme heat, mental health, and risk/protective factors. In addition, we conducted six focus groups with 21 people with lived experience of heat and/or mental illness and 12 healthcare professionals. Transcripts were analyzed using thematic content analysis and informed the co-development of the screening tool. RESULTS: Out of 764 articles identified by the systematic review, 47 were included. Evidence emerged for age, sex, existing mental illness, ethnicity, and socioeconomic status as risk factors. However, findings were inconsistent between studies, likely due to differences in study population and methodology. Protective effects included good physical health, social support, and exposure to green spaces. Our qualitative investigation identified additional risk and protective factors related to: (1) behavioral adaptability, (2) personal heat sensitivity, and (3) disparities in heat exposure. The resulting screening tool, HEAT-MH (Heat Exposure Assessment Tool for Mental Health), contains 15 questions on previous experiences of heat, general health, and lifestyle. CONCLUSIONS: The mental health impacts of extreme heat depend on a range of risk and protective factors, including demographic, socioeconomic, health, and lifestyle characteristics.
8 July 2026
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