To characterise long-term trajectory of recovery in individuals with long covid.

Prospective cohort.

Single-centre, specialist post-COVID service (London, UK).

Individuals aged ≥18 years with long covid (hospitalised and non-hospitalised) from April 2020 to March 2024.

Routine, prospectively collected data on symptoms, quality of life (including Fatigue Assessment Scale (FAS) and EuroQol 5 Dimensions (EQ-5D), return to work status and healthcare utilisation (investigations, outpatient and emergency attendances). The primary outcome was recovery by self-reported >75% of ‘best health’ (EQ-5D Visual Analogue Scale) and was assessed using Cox proportional hazards regression models over 4 years. Linked National Health Service England registry data provided secondary care healthcare utilisation and expenditure.

We included 3590 individuals (63.3% female, 73.5% non-hospitalised, median age 50.0 years, 71.9% with ≥2 doses of COVID-19 vaccination), who were followed up for a median of 136 (0–346) days since first assessment and 502 (251–825) days since symptom onset. At first assessment, 33.2% of employed individuals were unable to work. Dominant symptoms were fatigue (78.7%), breathlessness (68.1%) and brain fog (53.5%). 33.4% of individuals recovered to >75% of best health prior to clinic discharge (recovery occurred median 202 (94–468) days from symptom onset). Vaccinated individuals were more likely to recover faster (pre: HR 2.93 (2.00–4.28) and post: HR 1.34 (1.05–1.71) COVID-19 infection), whereas recovery hazard was inversely associated with FAS (HR 0.37 (0.33–0.42)), myalgia (HR 0.59 (0.45–0.76)) and dysautonomic symptoms (HR 0.46 (0.34–0.62)). There was high secondary care healthcare utilisation (both emergency and outpatient care). Annual inpatient and outpatient expenditure was significantly lower in hospitalised individuals while under the service. When compared with the prereferral period, emergency department attendances were reduced in non-hospitalised patients with long covid, but outpatient costs increased.

In the largest long covid cohort from a single specialist post-COVID service to date, only one-third of individuals under follow-up achieved satisfactory recovery. Fatigue severity and COVID-19 vaccination at presentation, even after initial COVID-19 infection, was associated with long covid recovery. Ongoing service provision for this and other post-viral conditions is necessary to support care, progress treatment options and provide capacity for future pandemic preparedness. Research and clinical services should emphasise these factors as the strongest predictors of non-recovery.

A ‘7-1-7’ timeliness metric, developed for hastening the response to infectious disease outbreaks/pandemics, was adapted to improve screening and managing household contacts (HHCs) of pulmonary tuberculosis (TB) patients. The feasibility, enablers, challenges and utility of implementing this modified metric through TB Champions (TB survivors) for HHC management were assessed.

This was an explanatory mixed-methods study with a cohort design (quantitative) followed by a descriptive design with focus group discussions (qualitative).

The study was conducted within routine programmatic settings in public health facilities in six districts from three states of India.

In total, 595 drug-susceptible index pulmonary TB patients registered for treatment in the selected health facilities, and their listed 2108 HHCs were included in the study between December 2022 and August 2023. All 17 TB Champions involved in implementation participated in the focus group discussions.

The primary outcome measures were the percentage of eligible participants receiving the desired service within the ‘7-1-7’ timeliness metric and challenges in achieving the timeliness metrics.

In 89% of 595 index patients, their HHCs were line-listed within 7 days of initiating anti-TB treatment (‘First-7’). In 90% of 2108 HHCs, screening outcomes were ascertained within 1 day of line-listing (‘Next-1’). In 42% of 2073 HHCs eligible for further evaluation, anti-TB treatment, TB preventive treatment (TPT) or a decision to not receive medication were made within 7 days of screening (‘Second-7’). Barriers to TPT uptake included lack of money and daily wage losses for travelling to clinics, reluctance of asymptomatic contacts to take medication and fear of adverse events. TB Champions felt timeliness metrics improved performance in the systematic and timely management of HHCs.

TB Champions found ‘7-1-7’ timeliness metrics were feasible and useful, and national TB programmes should consider their operationalisation.

Anxiety disorders are among the most common co-occurring mental health conditions experienced by autistic youth. Without appropriate intervention, anxiety disorders and related difficulties experienced by autistic youth can remain well into adulthood, causing reduced quality of life. Behavioral Interventions for Anxiety in Children with Autism (BIACA) is a manualised, modular evidence-based cognitive behavioural therapy with demonstrated efficacy in reducing or fully remitting anxiety symptoms and improving overall adaptive functioning for autistic youth. However, BIACA has been developed and tested mostly in academic research laboratories and has involved a limited number of community clinicians. Thus, certain characteristics (eg, length, complexity) may require adaptation to facilitate adoption and use in community settings.

This mixed methods study will use and evaluate a community-engaged, intervention adaptation method (ie, Adapted version of the Method for Program Adaptation through Community Engagement (AM-PACE)) to develop an adapted version of BIACA for community use. In the current study, the AM-PACE method will involve: (1) a Community Advisory Board (CAB), (2) structured process to identify core components, (3) community feedback via surveys and semistructured interviews and (4) role play exercises with intended clients. Thereafter, community-based providers (N=200) will be asked to evaluate the feasibility, acceptability, appropriateness, usability and intent to use for the original BIACA intervention and adapted BIACA intervention. Repeated measures Analysis of Variance (ANOVA) will be conducted to determine whether programme type predicts provider ratings. Higher provider ratings for the adapted BIACA intervention may indicate adaptations identified through AM-PACE-enhanced potential for BIACA to be equitably implemented in community settings.

Ethics approval was obtained from the Michigan State University Institutional Review Board. Research findings will be published in peer-reviewed journals, presented at international conferences and disseminated in alignment with CAB recommendations.

This study has been registered on Open Science Framework: https://doi.org/10.17605/OSF.IO/Z54MD

Histopathological evaluation of prostate biopsies using the Gleason scoring system is critical for prostate cancer diagnosis and treatment selection. However, grading variability among pathologists can lead to inconsistent assessments, risking inappropriate treatment. Similar challenges complicate the assessment of other prognostic features like cribriform cancer morphology and perineural invasion. Many pathology departments are also facing an increasingly unsustainable workload due to rising prostate cancer incidence and a decreasing pathologist workforce coinciding with increasing requirements for more complex assessments and reporting. Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images show promise in improving the accuracy and efficiency of histopathological assessments. Studies have demonstrated AI’s capability to diagnose and grade prostate cancer comparably to expert pathologists. However, external validations on diverse data sets have been limited and often show reduced performance. Historically, there have been no well-established guidelines for AI study designs and validation methods. Diagnostic assessments of AI systems often lack preregistered protocols and rigorous external cohort sampling, essential for reliable evidence of their safety and accuracy.

This study protocol covers the retrospective validation of an AI system for prostate biopsy assessment. The primary objective of the study is to develop a high-performing and robust AI model for diagnosis and Gleason scoring of prostate cancer in core needle biopsies, and at scale evaluate whether it can generalise to fully external data from independent patients, pathology laboratories and digitalisation platforms. The secondary objectives cover AI performance in estimating cancer extent and detecting cribriform prostate cancer and perineural invasion. This protocol outlines the steps for data collection, predefined partitioning of data cohorts for AI model training and validation, model development and predetermined statistical analyses, ensuring systematic development and comprehensive validation of the system. The protocol adheres to Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis+AI (TRIPOD+AI), Protocol Items for External Cohort Evaluation of a Deep Learning System in Cancer Diagnostics (PIECES), Checklist for AI in Medical Imaging (CLAIM) and other relevant best practices.

Data collection and usage were approved by the respective ethical review boards of each participating clinical laboratory, and centralised anonymised data handling was approved by the Swedish Ethical Review Authority. The study will be conducted in agreement with the Helsinki Declaration. The findings will be disseminated in peer-reviewed publications (open access).