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.

The development of effective vaccines targeting human papillomavirus (HPV) has significantly contributed to disease prevention, highly relevant in immunosuppressed patients who have higher incidence of HPV-related cancers than their non-immunosuppressed counterparts. However, the acceptance and uptake of the HPV vaccine among immunosuppressed individuals pose unique challenges. Immunocompromised patients’ acceptance of the HPV vaccine is influenced by multifaceted factors, including concerns about safety and effectiveness, interactions with immunosuppressive medications and uncertainties due to their compromised immunity. This systematic review aims to identify the main factors influencing HPV vaccine acceptance among immunosuppressed patients.

A comprehensive search strategy will be executed across databases such as MEDLINE/PubMed, Embase, Scopus, Web of Science, ScienceDirect, Latin American and Caribbean Literature in Health Sciences, Cumulative Index to Nursing and Allied Health Literature and Cochrane Database. The review will encompass the three WHO-endorsed HPV vaccines (quadrivalent, bivalent and nonavalent) and will consider studies related to HPV vaccines and their administration. The scope includes study focusing on immunosuppressed patients who received organ transplants, cancer treatments or are HIV-positive. No temporal restrictions will be applied, and searches will be conducted until December 2025. Observational studies, including retrospective/prospective cohorts, case–control and cross-sectional studies, reporting factors influencing HPV vaccination in immunosuppressed populations will be included. Studies with overlapping patient populations will be excluded. Data extraction will include study details, demographics, vaccine type, risk/protective factors, outcomes and medical history. Validation and cross-verification will ensure data accuracy. Risk of bias will be assessed using ROBINS-I (Risk Of Bias In Non-randomised Studies of Interventions), and GRADE (Grading of Recommendations Assessment, Development and Evaluation) will rate evidence certainty. Meta-analysis, guided by Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, will employ fixed/random-effects models, assessing heterogeneity using I² statistics.

This research will analyse previously published data, so ethical approval is not required. The results of the systematic review will be submitted for publication in a peer-reviewed journal.

CRD42023452537.

Post-COVID-19 conditions (PCC) may include pulmonary sequelae, fatigue and other symptoms, but its mechanisms are not fully elucidated.

This study investigated the correlation between fatigue and the presence of pulmonary abnormalities in PCC patients with respiratory involvement 6–12 months after hospitalisation.

Cross-sectional study.

A tertiary hospital in Brazil.

315 patients, aged ≥18 years, were considered eligible based on SARS-CoV-2 infection confirmed by reverse transcription-PCR.

Pulmonary function tests (PFT), cardiopulmonary exercise tests (CPET), chest CT and hand grip were performed. The following scales were applied: Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale, Euroqol 5 Dimensions quality of life (EQ-5D) and Hospital Anxiety and Depression Scale (HADS). Participants were divided between the fatigue group (FACIT-F≤30) and the non-fatigue group (FACIT-F>30). For the statistical analysis, the primary outcome was the difference in the diffusing capacity of the lungs for carbon monoxide (DLCO) between groups. Considered secondary outcomes were differences in PFT, CPET, chest CT, hand grip, EQ-5D and HADS.

The fatigue group had 81 patients (25.7%) against 234 (74.3%). PFT and CPET showed no significant difference in DLCO and oxygen consumption peak values between groups. The fatigue group had a lower workload (mean 55.3±21.3 watts vs 66.5±23.2 watts, p=0.003), higher breathing reserve (median 41.9% (33.8–52.5) vs 37.7% (28.9–47.1), p=0.028) and lower prevalence of ground glass opacity (60.8% vs 77.7%, p=0.003) and reticulation (36.7% vs 54.9%, p=0.005) in chest CT. The fatigue group had higher anxiety (57% vs 24%, p

Fatigue in patients with PCC 6–12 months after hospitalisation is relatively common and had weak correlation with pulmonary disorders. Our results suggested fatigue could be strongly related with peripheral disorders such as reduced musculoskeletal strength or psychosocial limitations.

It is unclear how mis- and disinformation regarding healthcare policy changes propagate throughout Latino communities via social media. This may lead to chilling effects that dissuade eligible individuals from enrolling in critical safety net programmes such as Medicaid. This study will examine pathways and mechanisms by which sentiment in response to mis- and disinformation regarding healthcare policies on social media differentially impact health disparity populations, thus supporting the design of tailored social media interventions to mitigate this.

We will search social media from X/Twitter, Facebook/Instagram and Reddit for keywords relating to health benefit programmes. Demographic, geographical location and other characteristics of users will be used to stratify social media data. Posts will be classified as fake-news-related or fact-related based on curated lists of fake-news-related websites. The number, temporal dissemination and positive or negative sentiment in reacting to posts and threads will be examined using the Python-based Valence Aware Dictionary and sEntiment Reasoner (VADER). Using a crowd-sourcing methodology, a novel Spanish-language VADER (S-VADER) will be created to rate sentiment to social media among Spanish-speaking Latinos. With the proposed approach, we will explore reactions to the dissemination of fake-news- or fact-related social media tweets and posts and their sources. Analyses of social media posts in response to healthcare-related policies will provide insights into fears faced by Latinos and Spanish speakers, as well as positive or negative perceptions relating to the policy over time among social media users.

Our study protocol was approved by the University of California, Los Angeles IRB (IRB#23–0 01 123). Results from this study will be disseminated in peer-reviewed journals and conference presentations, and S-VADER will be disseminated to public repositories such as GitHub.