UNASSIGNED: Rare diseases are an often chronic, progressive and life-limiting group of conditions affecting more than 30 million people in Europe. These diseases are associated with significant direct and indirect costs to a spectrum of stakeholders, ranging from individuals and their families to society overall. Further quantitative research on the economic cost for children and their families living with a rare disease is required as there is little known on this topic. This scoping review aims to document the extent and type of evidence on the economic impacts of living with a rare disease for children and their families.
UNASSIGNED: This scoping review will follow the PRISMA-ScR and Joanna Briggs Institute guidelines and follow the six-stage methodology for scoping reviews: (1) identifying the research question, (2) identifying relevant studies, (3) study selection, (4) charting the data, (5) collating, summarising and reporting results and (6) knowledge user consultation. Key inclusion criteria have been developed according to the Population-Concept-Context (PCC) framework. The databases EconLit, ABI/Inform, MEDLINE, PubMed, CINAHL, and Scopus will be searched for possible articles for inclusion. Two independent reviewers will screen titles and abstracts of potential articles using a dual review process to ensure all relevant studies are included. All included articles will be assessed using a validated quality appraisal tool. A panel of patient and public involvement representatives experiencing rare diseases and knowledge users will validate the review results.
UNASSIGNED: This scoping review will map the current literature on the economic impact of paediatric rare diseases to understand how these impacts affect children living with rare diseases and their families. This evidence has the potential to influence policy and future research in this area and will support further research on the economic impact of rare diseases on families.

BACKGROUND: Rare diseases affect millions of people globally, with a lifetime incidence of 1 in 17. They are complex, multisystem, severe disease, and patients experience a diagnostic delay averaging 5.6 years with associated misdiagnoses, inappropriate treatments, and anxiety. MendelScan is a digital case-finding tool that uses structured primary care data to identify patients at risk of being affected by one of a series of rare diseases. This qualitative study explored primary healthcare professionals\' experiences of implementing this technology.
OBJECTIVE: To develop an understanding of primary healthcare professionals\' experiences of implementing MendelScan with a focus on their perception of the technology and its implementation, challenges and opportunities.
METHODS: A qualitative appraisal of implementing MendelScan using descriptive analysis of surveys and semi-structured interviews with professionals.
RESULTS: A total of 11 professionals from the two project areas participated. Participants included GPs, nurses, a genetic counsellor, and healthcare managers.Participants reported that they received adequate support, the project increased confidence in integrating genetics in primary care, and that it may improve inequity in rare disease diagnosis. Professionals\' reservations were related to knowledge, time commitments, primary care pressures, contacting patients, data sharing and confidentiality, and overcoming clinical resistance in sharing data with a non-NHS external body.
CONCLUSIONS: This study provides a valuable insight into the experiences of primary care professionals using a novel tool to identify rare diseases. The potential of this tool is promising with benefits for skill development. Large-scale implementation faces challenges related to primary care capacity, data, and funding.

BACKGROUND: Diagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability.
OBJECTIVE: This study aims to develop an information retrieval (IR)-based framework that accommodates data sparsity to facilitate broader diagnostic decision support.
METHODS: We introduced an IR-based diagnostic decision support framework called CliniqIR. It uses clinical text records, the Unified Medical Language System Metathesaurus, and 33 million PubMed abstracts to classify a broad spectrum of diagnoses independent of training data availability. CliniqIR is designed to be compatible with any IR framework. Therefore, we implemented it using both dense and sparse retrieval approaches. We compared CliniqIR\'s performance to that of pretrained clinical transformer models such as Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) in supervised and zero-shot settings. Subsequently, we combined the strength of supervised fine-tuned ClinicalBERT and CliniqIR to build an ensemble framework that delivers state-of-the-art diagnostic predictions.
RESULTS: On a complex diagnosis data set (DC3) without any training data, CliniqIR models returned the correct diagnosis within their top 3 predictions. On the Medical Information Mart for Intensive Care III data set, CliniqIR models surpassed ClinicalBERT in predicting diagnoses with <5 training samples by an average difference in mean reciprocal rank of 0.10. In a zero-shot setting where models received no disease-specific training, CliniqIR still outperformed the pretrained transformer models with a greater mean reciprocal rank of at least 0.10. Furthermore, in most conditions, our ensemble framework surpassed the performance of its individual components, demonstrating its enhanced ability to make precise diagnostic predictions.
CONCLUSIONS: Our experiments highlight the importance of IR in leveraging unstructured knowledge resources to identify infrequently encountered diagnoses. In addition, our ensemble framework benefits from combining the complementary strengths of the supervised and retrieval-based models to diagnose a broad spectrum of diseases.

BACKGROUND: Rare diseases pose immense challenges for healthcare systems due to their low prevalence, associated disabilities, and attendant treatment costs. Advancements in gene therapy, such as treatments for Spinal Muscular Atrophy (SMA), have introduced novel therapeutic options, but the high costs, exemplified by Zolgensma® at US$2.1 million, present significant financial barriers. This scoping review aimed to compare the funding approaches for rare disease treatments across high-performing health systems in Australia, Singapore, South Korea, the United Kingdom (UK), and the United States (US), aiming to identify best practices and areas for future research.
METHODS: In accordance with the PRISMA-ScR guidelines and the methodological framework by Arksey and O\'Malley and ensuing recommendations, a comprehensive search of electronic databases (Medline, EMBASE, and Cochrane) and grey literature from health department websites and leading national organizations dedicated to rare diseases in these countries was conducted. Countries selected for comparison were high-income countries with advanced economies and high-performing health systems: Australia, Singapore, South Korea, the UK, and the US. The inclusion criteria focused on studies detailing drug approval processes, reimbursement decisions and funding mechanisms, and published from 2010 to 2024.
RESULTS: Based on a thorough review of 18 published papers and grey literature, various strategies are employed by countries to balance budgetary constraints and access to rare disease treatments. Australia utilizes the Life Saving Drugs Program and risk-sharing agreements. Singapore depends on the Rare Disease Fund, which matches public donations. South Korea\'s National Health Insurance Service covers specific orphan drugs through risk-sharing agreements. The UK relies on the National Institute for Health and Care Excellence (NICE) to evaluate treatments for cost-effectiveness, supported by the Innovative Medicines Fund. In the US, a combination of federal and state programs, private insurance and non-profit support is used.
CONCLUSIONS: Outcome-based risk-sharing agreements present a practical solution for managing the financial strain of costly treatments. These agreements tie payment to actual treatment efficacy, thereby distributing financial risk and promoting ongoing data collection. Countries should consider adopting and expanding these agreements to balance immediate expenses with long-term benefits, ultimately ensuring equitable access to crucial treatments for patients afflicted by rare diseases.

Decision-making in clinical medicine ideally is based upon evidence from randomized, placebo-controlled trials (RCTs) and subsequent systematic reviews and meta-analyses. However, for orphan diseases, the expectation of having one or multiple RCTs that inform clinical guidelines or justify specific treatments can be unrealistic and subsequent therapeutic nihilism can be detrimental to patients. This article discusses the benefits of therapeutic decision-making in the context of orphan diseases, focusing on primary sclerosing cholangitis (PSC) as an example of an orphan disease with poor clinical outcomes. PSC is a rare disorder characterized by inflammation and progressive fibrosis of the bile ducts. It carries a high risk of liver failure, malignancies, and debilitating symptoms that impair quality of life. Liver transplantation is currently the only life-prolonging intervention for PSC, but it is not a curative option. The article highlights the potential benefits of treating PSC patients with oral vancomycin (OV), which has shown significant clinical responses and improved quality of life in some cases. However, access to OV therapy is limited due to the lack of RCTs supporting its use. The standard requirement of having evidence from RCTs may result in withholding potentially life-altering and/or life-saving treatments for patients with orphan diseases. Conducting RCTs is challenging in these patient populations due to difficulties in recruiting the required patient cohorts and limited commercial returns. A standardized \'adaptive treatment strategy\' is proposed to address this. This approach leverages the best available evidence for specific treatments, considers individual clinical responses, and adjusts treatment over time.

The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.

Cerebrospinal fluid (CSF) is a body fluid that can be used for the diagnosis of various diseases. However, CSF collection requires an invasive and painful procedure called a lumbar puncture (LP). This procedure is applied to any patient with a known risk of central nervous system (CNS) damage or neurodegenerative disease, regardless of their age range. Hence, this can be a very painful procedure, especially in infants and elderly patients. On the other hand, the detection of disease biomarkers in CSF makes diagnoses as accurate as possible. This review aims to explore novel electrochemical biosensing platforms that have impacted biomedical science. Biosensors have emerged as techniques to accelerate the detection of known biomarkers in body fluids such as CSF. Biosensors can be designed and modified in various ways and shapes according to their ultimate applications to detect and quantify biomarkers of interest. This process can also significantly influence the detection and diagnosis of CSF. Hence, it is important to understand the role of this technology in the rapidly progressing field of biomedical science.

Background-This review provides a comprehensive overview of rare anemias, emphasizing their hereditary and acquired causes, diagnostic advancements, and evolving treatment strategies. It outlines the significance of rare anemias within public health, historical challenges in recognition and treatment, and the role of European initiatives like ENERCA and EuroBloodNet in advancing care. Content-This document discusses diagnostic technologies like next-generation sequencing and the impact of artificial intelligence, alongside the promising avenues of gene therapy, targeted drug treatments, and stem cell transplantation. It underscores the importance of a patient-tailored approach, advances in diagnostic tools, and the necessity for continued research, patient advocacy, and international collaboration to improve outcomes for individuals with rare anemias.

BACKGROUND: Osteogenesis imperfecta (OI) is a rare disease characterized by low bone mass and bone fragility, associated with an increased risk of fractures, and skeletal and extra-skeletal symptoms that results in an impairment of health-related quality of life of OI patients. Since published studies on OI in Spain are limited, this study aimed to determine the epidemiology, assessed the disease burden, management and unmet needs of OI patients in Spain. Thirty-four experts in the management of patients with osteogenesis imperfecta completed two rounds of online consultation and reported real-life experience and data from Spanish hospitals. Delphi study questionnaires were based on literature review. A working group of nationally recognized clinical experts supported the development of the study questionnaires and the final validation of results.
RESULTS: The estimated prevalence of patients diagnosed with OI in Spain is 0.56:10,000 inhabitants (95%CI: 0.54-0.59), which represents that, approximately, 2,669 OI patients are currently managed in Spanish hospitals. It is estimated that approximately 269 new patients would be diagnosed with OI each year in Spain, representing an estimated incidence of 0.06 (95%CI: 0.05-0.06) per 10,000 inhabitants per year. Clinical management of OI in Spain is performed by a range of medical specialists; however, multidisciplinary care is not fully implemented. The absence of an approved curative treatment or a treatment to reduce the clinical features of the disease remains the main unmet need.
CONCLUSIONS: This study provides a snapshot of the current situation of patients with OI in Spain reported by clinical experts. The results provide an estimation of the epidemiology of the disease, and complement the available evidence on disease burden, clinical management, and unmet needs of these patients in Spain.

This study explores the experiences of Polish caregivers of children with rare disease (CRD) with health care and social services for CRD. A mixed-methods approach was employed, using an open-ended questionnaire with a convenience sample. Quantitative data presented through descriptive statistics, were complemented by thematic analysis applied to qualitative responses. Responses from 925 caregivers of 1002 children with CRD revealed that the duration of the diagnostic journey varied, spanning from 0 to 18 years, with an average time of 1.7 years. Similarly, the average number of physicians consulted before receiving the correct diagnosis was 4.8. The Internet was basic source of information about children\'s disease. Although caregivers were to some extent satisfied with the quality of health care for CRD, they complained at the accessibility of health care and social services, physicians\' ignorance regarding RDs, the lack of co-ordinated care and financial and psychological support. To break the cycle of the diagnostic and therapeutic odyssey that may aggravate the condition of CRD, cause parental stress and financial burden there is a need to change our view on CRD from cure to family-oriented care. Multifaceted challenges and needs of CRD families should be prioritized.