We investigated symptoms reported before and after heart failure (HF) diagnosis and their associations with 3-month hospitalisation and mortality.

To examine associations between symptoms recorded in primary care and short-term hospitalisation and mortality in HF patients.

Landmark analysis using Royston-Parmar survival models at baseline (diagnosis), 6 and 12 months post-diagnosis.

Primary care database (Clinical Practice Research Datalink) linked to hospital and mortality data (1998–2020).

Adults (>40 years) with a first HF diagnosis.

Shortness of breath, ankle swelling, oedema, fatigue, chest pain, depression and anxiety in the 3 months before diagnosis and at 6 and 12 months.

3-month all-cause hospitalisation and mortality; secondary outcomes included HF and non-cardiovascular hospitalisation.

Among 86 882 HF patients (62 742 and 54 555 surviving to 6 and 12 months, respectively), the magnitude of symptom risk varied by timepoint. Specifically, the symptoms with the strongest associations with adverse outcomes were: depression for all-cause hospitalisation at diagnosis (HR: 1.26; 95% CI 1.15 to 1.39) and 6 months (1.46; 1.25 to 1.70); ankle swelling for mortality (1.49; 1.14 to 1.94) at 6 months and SOB for HF hospitalisation (1.18; 1.12 to 1.26) at diagnosis and 12 months (1.99; 1.68 to 2.35).

Symptoms persisted and were more prominent at 6 and 12 months post-diagnosis than at diagnosis.

Medical devices account for approximately 6–10% of national health systems’ carbon footprints. The global use of single-use devices has increased, with implications for health systems’ climate impact. This systematic review aimed to synthesise global evidence on medical device carbon footprints, compare single-use and reusable devices and identify lifecycle carbon hotspots to inform policy and practice.

We conducted a systematic review of carbon footprints of medical devices used in clinical settings, reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.

We searched MEDLINE and Scopus, in 2022 and updated in 2025, and used citation tracking.

English-language, primary research involving carbon modelling of medical devices used in clinical settings was included, with no date restrictions.

Articles were screened, and data on carbon modelling methods, device footprints and lifecycle hotspots were extracted by two independent reviewers. Findings were synthesised in figures and tables, and narratively in text. The heterogeneity in carbon modelling approaches prevented quantitative synthesis.

Of 5195 articles identified, 59 met inclusion criteria. Life cycle assessment was the main carbon modelling approach, though application and data quality varied. Carbon footprints of 61 devices were assessed, primarily in surgical (16), anaesthetic (8) and endoscopic (8) specialties. Reusable devices consistently had lower lifecycle footprints. Hotspots were production and manufacturing for single-use devices and reprocessing for reusables.

Reusable devices are preferable from a climate perspective, though efforts are needed to reduce reprocessing emissions. Co-ordinated interventions are required: policymakers can enable supportive regulation; manufacturers can improve device design; healthcare facilities can optimise reprocessing; and providers can prioritise reusable device procurement and use.

Tobacco use is the most significant modifiable risk factor for adverse health outcomes, and early research indicates there are also significant harms associated with vaping. National targets aim to reduce smoking and vaping during pregnancy for Aboriginal and Torres Strait Islander people. While most Aboriginal and Torres Strait Islander people want to quit, cessation is frequently attempted without support, increasing the chance of relapse. Group-based smoking cessation programmes increase quit success by 50%–130% in the general population; however, they have never been evaluated in Aboriginal and/or Torres Strait Islander communities.

The Gulibaa study is an Indigenous-led and community-embedded project that will co-design, implement and evaluate a group-based model of care to support Aboriginal and Torres Strait Islander women to be smoke- and vape-free. Staff of Health Services in New South Wales, Australia, will receive training to deliver a face-to-face group-based smoking and vaping cessation intervention. Aboriginal and/or Torres Strait Islander people who identify as a woman or non-binary, are pregnant or of reproductive age (16 to 49 years), currently smoke or vape at least once per day and are willing to attend the programme are eligible to participate. Up to 500 participants will be recruited. A mixed method evaluation approach will be implemented guided by the RE-AIM framework. Outcomes will include intervention reach, intervention effectiveness (determined primarily by self-reported 7-day point prevalence abstinence at 6 months follow-up), acceptability and feasibility of the intervention, programme fidelity and maintenance and cost effectiveness.

Embedding culturally safe support to quit during pregnancy can result in improved outcomes for both mother and child and immediately improve intergenerational health and well-being. Ethics approval has been provided by the Aboriginal Health and Medical Research Council and the University of Newcastle. Study findings will be disseminated to Aboriginal and Torres Strait Islander communities in ways that are meaningful to them, as well as through Aboriginal health services, key national bodies, relevant state and federal government departments.

ACTRN12625001050448.

The expenses associated with cancer treatment are increasing at a rapid pace. The financial strain of providing care is experienced worldwide, but is particularly pronounced in low and middle-income countries (LMICs). This has resulted in a growing acknowledgement of the importance of value-based cancer care. Choosing Wisely Africa (CWA) is an initiative aimed at reducing the excessive use and expenses associated with cancer treatment. In this study, we assessed adherence to CWA recommendations for the treatment of breast cancer in three high-volume cancer centres in Sub-Saharan Africa (SSA).

A cross-sectional study across Rwanda, Ghana and Tanzania was conducted, involving a review of medical records to assess adherence to five measurable CWA practices in breast cancer care. The study used inferential statistics, such as ² tests, to compare adherence among these countries.

This study was conducted in three cancer centres (Ocean Road Cancer Institute, Rwanda Military Hospital and Korle Bu Teaching Hospital) in three countries (Tanzania, Rwanda and Ghana, respectively).

A total of 542 patients were recruited. Eligible patients were those with a breast cancer diagnosis and complete data as pertaining to five CWA recommendations.

A total of 542 participants with a mean age of 51 years were included. Participants were well distributed across Ghana (37%), Rwanda (34%) and Tanzania (29%). Female patients represented 97% of the study cohort. Half (51%) of the participants had some form of insurance. The study observed high adherence to cancer staging (94%) before treatment and histological confirmation (91%) before breast lump removal across all sites. Hypofractionation was used in 0% of cases in Rwanda, 42% in Ghana and 70% in Tanzania.

This study provides critical insights into the implementation of CWA recommendations in breast cancer care in SSA. It highlights the disparities in adherence to CWA recommendations across different centres, showing the need for policy-driven changes and healthcare infrastructure improvement to standardise cancer care practices in LMICs.

Being exposed to adverse psychosocial working conditions contributes to poor mental health in young workers. This study explores whether psychosocial work adversities are a necessary condition for work-related emotional exhaustion in young workers.

Data from the ‘Netherlands Working Condition Survey 2021’ was used. By applying a novel method called Necessary Condition Analysis, we tested two psychosocial work adversities as necessary conditions for high work-related emotional exhaustion in young workers: (1) a composite score of high job demands and low job resources and (2) a composite score of high job demands. Additionally, we tested whether the threshold for job demands as a necessary condition for high work-related emotional exhaustion differed for young workers with low versus high resources.

Secondary data analysis on a national working population-based survey.

The sample included 5791 young workers in the Netherlands (aged

Work-related emotional exhaustion.

A high level of the composite on job demands and job resources is necessary for a high level of work-related emotional exhaustion in young workers (effect size=0.11, p

Both psychosocial work adversities were necessary conditions for high work-related emotional exhaustion in young workers. The necessity threshold for job demands was higher for young workers with high job resources, compared with the group with low resources. This indicates that removing psychosocial work adversities and ensuring the presence of job resources might contribute to the prevention of high work-related emotional exhaustion in young workers.

Structural MRI of the brain is routinely performed on patients referred to memory clinics; however, resulting radiology reports, including volumetric assessments, are conventionally stored as unstructured free text. We sought to use natural language processing (NLP) to extract text relating to intracranial volumetric assessment from brain MRI text reports to enhance routine data availability for research purposes.

Electronic records from a large mental healthcare provider serving a geographic catchment of 1.3 million residents in four boroughs of south London, UK.

A corpus of 4007 de-identified brain MRI reports from patients referred to memory assessment services. An NLP algorithm was developed, using a span categorisation approach, to extract six binary (presence/absence) categories from the text reports: (i) global volume loss, (ii) hippocampal/medial temporal lobe volume loss and (iii) other lobar/regional volume loss. Distributions of these categories were evaluated.

The overall F1 score for the six categories was 0.89 (precision 0.92, recall 0.86), with the following precision/recall for each category: presence of global volume loss 0.95/0.95, absence of global volume loss 0.94/0.77, presence of regional volume loss 0.80/0.58, absence of regional volume loss 0.91/0.93, presence of hippocampal volume loss 0.90/0.88, and absence of hippocampal volume loss 0.94/0.92.

These results support the feasibility and accuracy of using NLP techniques to extract volumetric assessments from radiology reports, and the potential for automated generation of novel meta-data from dementia assessments in electronic health records.

To compare the quality and time efficiency of physician-written summaries with customised large language model (LLM)-generated medical summaries integrated into the electronic health record (EHR) in a non-English clinical environment.

Cross-sectional non-inferiority validation study.

Tertiary academic hospital.

52 physicians from 8 specialties at a large Dutch academic hospital participated, either in writing summaries (n=42) or evaluating them (n=10).

Physician writers wrote summaries of 50 patient records. LLM-generated summaries were created for the same records using an EHR-integrated LLM. An independent, blinded panel of physician evaluators compared physician-written summaries to LLM-generated summaries.

Primary outcome measures were completeness, correctness and conciseness (on a 5-point Likert scale). Secondary outcomes were preference and trust, and time to generate either the physician-written or LLM-generated summary.

The completeness and correctness of LLM-generated summaries did not differ significantly from physician-written summaries. However, LLM summaries were less concise (3.0 vs 3.5, p=0.001). Overall evaluation scores were similar (3.4 vs 3.3, p=0.373), with 57% of evaluators preferring LLM-generated summaries. Trust in both summary types was comparable, and interobserver variability showed excellent reliability (intraclass correlation coefficient 0.975). Physicians took an average of 7 min per summary, while LLMs completed the same task in just 15.7 s.

LLM-generated summaries are comparable to physician-written summaries in completeness and correctness, although slightly less concise. With a clear time-saving benefit, LLMs could help reduce clinicians’ administrative burden without compromising summary quality.