Scientific research grows at a tremendous pace, and by classifying over 140 million documents (research articles, book chapters, pre-prints and more) into over 29,000 topics we help make research more discoverable by enabling tracking broad trends as well as supporting deep dives in specialised areas.We are releasing a dataset containing version 1 of the Nature Research Intelligence topics. The topics were identified by using machine learning to cluster and organise a large citation network, built from documents citing each other. The resulting clusters were labelled using generative AI. The full methodology is described in a pre-print: Jenset, Bevan & Jain (2025).The dataset has one row for each of the 29,140 topics (at the most granular level), as well as the header row. The columns in the file are as follows:topic_label: the label for the topic, created with generative AI based on documents in the topic.size: the number of documents in the topic, as of May 19, 2025.topic_coherence: a metric from 0 (no coherence) to 1 (max coherence) indicating how coherent the topic is.topic_if: an impact factor type of metric indicating average citations to documents in the topic, calculated using the standard formula.for_l3_parent, for_l2_parent, for_l1_parent: a hierarchy organising the topics into progressively broader fields, using the ANZSRC fields of research. For further details see:Gard B. Jenset, Peter J. Bevan, Akarsh Jain et al. A large-scale, granular topic classification system for scientific documents, 27 April 2025, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-6529718/v1] [link]

Linear model built by a stepwise approach, with a square root transformation of the total citation outcome variable.

Sensitivity analysis by excluding RCTs with total citations greater than 500 without transforming the outcome variable.

IntroductionPost-stroke cognitive impairment (PSCI) and dementia may have a significant impact on stroke recurrence and long-term functional outcomes of patients.AimTo investigate the potential link between PSCI and dementia, and stroke recurrence, mortality, and poor functional outcomes of stroke survivors.MethodsA systematic search across Medline, Google Scholar, and Science Direct databases was done for studies that evaluated the association of PSCI and dementia with long-term stroke outcomes. The results were expressed as pooled hazard ratios (HR) with 95% confidence intervals (CI), and heterogeneity was assessed using the I2 statistic and the Chi-square test. Subgroup analyses were performed based on the sample size, geographical location, follow-up, and type of dementia/cognitive impairment. Study quality was evaluated using the Newcastle Ottawa Scale (NOS).ResultsThe meta-analysis included thirteen studies. Of them, ten studies (n = 4036) reported a significant association between PSCI and stroke recurrence, with a pooled HR of 1.33 (95% CI: 1.14–1.55, I2 = 84.6%). Subgroup analysis revealed a statistically significant association between PSCI and stroke recurrence across various subrgoups. Four studies (n = 1944) demonstrated that patients with PSCI had a higher risk of poor functional outcome, with a pooled HR of 1.68 (95% CI: 1.16–2.05, I2 = 80.0%). However, the multivariate analysis did not detect a significant association between PSCI and stroke mortality, with a pooled HR of 1.50 (95% CI: 0.94–2.40, I2 = 45.9%).ConclusionsThe study showed that PSCI was associated with 33% increased stroke recurrence and 68% higher rate of poor functional outcome. Our findings underscore the adverse impact of PSCI on stroke recurrence and functional outcomes, emphasizing the importance of early detection and targeted interventions to mitigate the cognitive impairment burden in stroke survivors.

IntroductionThe outbreak of a global pandemic like COVID-19 has highlighted significant distress around mental health. The burden of mental health issues like anxiety and depression requires evidence-based intervention, especially in low-income settings like Nepal. The study aims to determine the prevalence of anxiety and depression and the factors associated with it among hypertensive patients.Materials and methodsThe quantitative cross-sectional study design was used for this study. The study was conducted among 374 samples from selected wards of Kathmandu Metropolitan using a convenience sampling technique. Face-to-face interviews were conducted using a structured interview schedule. A Chi-square test was used to identify the statistical significance between dependent and independent variables. Binary logistic regression analysis was performed to determine the factors associated with anxiety and depression.ResultsThe prevalence of anxiety and depression among hypertensive patients during the COVID-19 pandemic was 27.8% and 24.3% respectively. According to the results of bivariate logistic regression analysis, smoking/tobacco consumption, staying in quarantine, positive COVID-19 test result, history of COVID-19 positive in the family, History of death due to COVID-19 in the family, visiting a hospital during the COVID-19 pandemic appeared as influencing factors of both anxiety and depression.ConclusionOur findings suggest that COVID-19 has a substantial effect on the mental health of hypertensive patients. This study highlights the need to develop early intervention and coping strategies among this population to minimize the negative impact of COVID-19 on their mental health and well-being.