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.

Understanding the prognostic factors associated with the failure of total elbow replacement (TER) is crucial for informing patients about risks and enabling shared decision-making regarding TER as a definitive management option. This protocol outlines the planned analysis of National Joint Registry (NJR) data to investigate prognostic factors for TER failure.

The primary analysis will use the NJR elbow dataset, including all eligible patients who underwent TER surgery between April 2012 and December 2023. To incorporate ethnicity and comorbidities as potential prognostic factors, the NJR will be linked to the National Health Service (NHS) England Hospital Episode Statistics-Admitted Patient Care (HES-APC) data for a secondary analysis. The analysis will adhere to the REporting recommendations for tumour MARKer prognostic studies guidelines. The primary outcome under investigation is TER failure, defined as requiring revision surgery. Initially, the overall prognosis of TER will be examined using unadjusted net implant failure via the Kaplan-Meier method. The list of potential prognostic factors to be investigated in this study has been informed by a systematic review on this topic, input from patient and public involvement and engagement (PPIE) groups and a survey shared with healthcare professionals providing TER services. The relationship between each potential prognostic factor and failure will be assessed using univariable regression methods. Based on the findings from our systematic review, the univariable association will also be adjusted for age, sex and indication for TER surgery using multivariable regression methods. The extent of missing data will be reported, and the reasons for missing data will be explored. A very high degree of data completeness is expected, and a complete case analysis will be performed as the primary analysis. Multiple imputations will be considered as a sensitivity analysis.

The NJR research committee approved this analysis, and the NHS Health Research Authority tool guidance dictates that the secondary use of such data for research does not require approval from a research ethics committee. The results from this analysis will be published in a peer-reviewed journal and presented at scientific conferences.

NCT06760585.

It is unclear whether routine testing of women for group B streptococcus (GBS) colonisation either in late pregnancy or during labour reduces early-onset neonatal sepsis, compared with a risk factor-based strategy.

Cluster randomised trial.

320 000 women from up to 80 hospital maternity units.

Sites will be randomised 1:1 to a routine testing strategy or the risk factor-based strategy, using a web-based minimisation algorithm. A second-level randomisation allocates routine testing sites to either antenatal enriched culture medium testing or intrapartum rapid testing. Intrapartum antibiotic prophylaxis will be offered if a test is positive for GBS, or if a maternal risk factor for early-onset GBS infection in her baby is identified before or during labour. Economic and acceptability evaluations will be embedded within the trial design.

The primary outcome is all-cause early (

The trial received a favourable opinion from Derby Research Ethics Committee on 16 September 2019 (19/EM/0253). The allocated testing strategy will be adopted as standard clinical practice by the site. Women in the routine testing sites will give verbal consent for the test. The trial will use routinely collected data retrieved from National Health Service databases, supplemented with limited participant-level collection of process outcomes. Individual written consent will not be sought. The trial results, and parallel economic, qualitative, implementation and methodological results, will be published in the journal Health Technology Assessment.

ISRCTN49639731.

The COVID-19 pandemic has had a significant impact on medical education, with many institutions shifting to online learning to ensure the safety of students and staff. However, there appears to be a decline in in-person attendance at medical schools across the UK and worldwide following the relaxation of social distancing rules and the reinstatement of in-person teaching. Importantly, this trend was also observed before the pandemic. While reflected within the literature, there is currently no systematic review describing these changes. We aim to find out how medical students’ attendance is changing as documented within the literature and its impact on their educational outcomes.

This systematic review followed the guidelines of the Centre of Research and Dissemination, Moose and Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We searched the major databases of Medline via Ovid, Embase via Ovid, Scopus, Web of Science, British Education Index via EBSCOhost and ERIC via EBSCOhost in September 2023. Two reviewers independently screened each paper and extracted the data, with a third reviewer for dispute resolution. All studies reporting on medical students from various universities, both graduate and undergraduate, and describing changes in attendance and/or students’ educational outcomes were included. Risk of bias in individual studies was assessed using the Agency for Healthcare Research and Quality tool. A narrative synthesis of the findings from all included studies was done.

12 papers were included in the analysis. Primary aim: Of the eight papers that measured attendance data over more than one academic year, only one paper demonstrated a statistically significant decrease while one paper demonstrated a statistically significant increase in attendance over the observational period. Other papers either did not perform statistical tests or did not demonstrate statistical significance. Secondary aims: Most papers showed a general positive correlation between attendance and educational outcomes. No studies explicitly explored reasons for changes in attendance seen. Only one paper outlined a possible strategy to address changes in attendance, a mandatory attendance policy, which has mixed outcomes.

Despite widespread anecdotally reported attendance decline post-COVID-19, overall, there was no consistent change in attendance noted. However, there was a large heterogeneity in the studies included. Further research is required to elucidate trends in attendance and its impact on medical education.