While health services leaders rely heavily on information gathered via environmental scans (ESs) to guide strategic decision-making, formal guidance on how to conduct these scans is notably absent. The purpose of this study was to determine the level of agreement on essential components of a definition and a methodological framework for ESs. The goals were to (1) advance our working definition to a concept definition for ESs and (2) develop a methodological framework to guide health service researchers conducting ESs.

We used a real-time, modified Delphi survey in a virtual platform setting to seek perspectives on statements related to ESs from individuals who were recruited based on having verifiable experience designing or conducting ESs in health services delivery research. Surveylet, an online software, was used to facilitate asynchronous data collection and to determine the level of agreement on the statements with an a priori threshold of 75% set for agreement on each statement.

21 panellists provided opinions on 59 statements related to a proposed ES definition and on 69 statements specific to components of a methodological framework for ESs.

Panellists from four countries participated in the survey representing 2 to ≥11 years of experience with ESs and having completed 1 to ≥7 ESs. Agreement was achieved in 28 of the 59 statements related to the ES definition and for 51 of 69 statements related to a methodological framework.

The agreement on many elements deemed essential for a definition of ES support development of a proposed concept definition of ES in health service delivery research. As well, the agreement on components deemed necessary for a methodological framework will help in future development of such a framework to guide stakeholders in the planning and implementation of ESs. These results provide a starting point for a common understanding of ESs in the field of health services delivery research.

To assess the capability of a convolutional neural network trained by transfer learning on anterior segment optical coherence tomography (AS-OCT) images, Placido-disk corneal topography images and external photographs to predict age and biological sex.

Development of a deep learning model trained on retrospectively collected data using transfer learning.

A multicentre secondary care public health trust based in London.

We included 557,468 scans from 40,592 eyes of 20,542 patients. Data were extracted from all patients who underwent MS-39 imaging within our trust from October 2020 to March 2023.

Primary outcome measures for biological sex classification included accuracy, precision, recall, F1-score and area under the receiver operating curve (ROC-AUC). Primary outcome measures for age prediction were Pearson correlation coefficients (r), coefficients of determination (R²) and the mean absolute error (MAE) to evaluate the predictive performance. The secondary outcome was to visualise and interpret the model’s decision-making process through the construction of saliency maps.

For age prediction, the MAEs for the Placido, AS-OCT and external photograph models were 5.2, 5.1 and 6.2 years, respectively. For gender classification, the same models achieved ROC-AUCs of 0.88, 0.73 and 0.81, respectively. No difference in performance was found in the analysis of corneas with pathological topography. The saliency maps highlighted the peri-limbal cornea for age prediction and the central cornea for gender discrimination.

Our study demonstrates that deep learning models can extract age and gender information from anterior segment images. These findings support the concept that the anterior segment, like the retina, encodes important biological information. Future research should explore whether these models can predict specific systemic conditions.

Shared decision-making (SDM) between clinicians and patients is considered ‘best practice’. There is limited evidence regarding SDM in surgery, particularly in the emergency setting. Emergency SDM may be particularly challenging due to: time pressures, the patient’s underlying condition and the nature of the patient-surgeon interaction. However, emergency surgery arguably has a greater need for SDM due to the likelihood of disparate outcomes from intervention, which is dependent on the various treatment options available. This is necessary for patients to make informed decisions regarding their treatment of surgical pathology. The primary objective of this scoping review is to understand the extent and type of evidence in relation to SDM in emergency surgery to determine methods for improving SDM.

Any studies reporting SDM in emergency surgery on adult patients (age >18 years) will be included. EMBASE, Medline, Cochrane, CINAHL and Scopus databases will be searched for articles with no language or date limits. Studies will be screened by two independent reviewers, with consensus met prior to data extraction. Data extracted to include study design, details of study population, tools used to measure SDM, prevalence of SDM and barriers and enablers for SDM.

A systematic narrative synthesis will be performed following JBI (Joanna Briggs Institute) guidance. These will summarise findings of included studies. The findings may inform future research into facilitating implementation of SDM in emergency surgery.

This study does not require ethical approval. Final findings will be submitted for peer-reviewed publication and presentation at surgical conferences.

To investigate whether quantitative retinal markers, derived from multimodal retinal imaging, are associated with increased risk of mortality among individuals with proliferative diabetic retinopathy (PDR), the most severe form of diabetic retinopathy.

Longitudinal retrospective cohort analysis.

This study was nested within the AlzEye cohort, which links longitudinal multimodal retinal imaging data routinely collected from a large tertiary ophthalmic institution in London, UK, with nationally held hospital admissions data across England.

A total of 675 individuals (1129 eyes) with PDR were included from the AlzEye cohort. Participants were aged ≥40 years (mean age 57.3 years, SD 10.3), and 410 (60.7%) were male.

The primary outcome was all-cause mortality. Quantitative retinal markers were derived from fundus photographs and optical coherence tomography using AutoMorph and Topcon Advanced Boundary Segmentation, respectively. We used unadjusted and adjusted Cox-proportional hazards models to estimate hazard ratios (HR) for the association between retinal features and time to death.

After adjusting for sociodemographic factors, each 1-SD decrease in arterial fractal dimension (HR: 1.54, 95% CI: 1.18 to 2.04), arterial vessel density (HR: 1.59, 95% CI: 1.15 to 2.17), arterial average width (HR: 1.35, 95% CI: 1.02 to 1.79), central retinal arteriolar equivalent (HR: 1.39, 95% CI: 1.05 to 1.82) and ganglion cell-inner plexiform layer (GC-IPL) thickness (HR: 1.61, 95% CI: 1.03 to 2.50) was associated with increased mortality risk. When also adjusting for hypertension, arterial fractal dimension (HR: 1.45, 95% CI: 1.08 to 1.92), arterial vessel density (HR: 1.47, 95% CI: 1.05 to 2.08) and GC-IPL thickness (HR: 1.56, 95% CI: 1.03 to 2.38) remained significantly associated with mortality.

Several quantitative retinal markers, relating to both microvascular morphology and retinal neural thickness, are associated with increased mortality among individuals with PDR. The role of retinal imaging in identifying those individuals with PDR most at risk of imminent life-threatening sequelae warrants further investigation.