2023 Speed
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2023 Usage
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Patients with dementia and their caregivers could benefit from advance care planning though may not be having these discussions in a timely manner or at all. A prognostic tool could serve as a prompt to health...
There is urgent clinical need to identify reliable prognostic biomarkers that predict the progression of dementia symptoms in individuals with early-phase Alzheimer’s disease (AD) especially given the research...
Lung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in...
Avoidable hospitalizations are considered preventable given effective and timely primary care management and are an important indicator of health system performance. The ability to predict avoidable hospitaliz...
Speech perception tests are essential to measure the functional use of hearing and to determine the effectiveness of hearing aids and implantable auditory devices. However, these language-based tests require a...
Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction models must be updated in order to...
For people at high risk of lung cancer, low-dose computed tomography (LDCT) is proposed as a method to reduce mortality.
Community-acquired lower respiratory tract infections (LRTI) are common in primary care and patients at particular risk of adverse outcomes, e.g., hospitalisation and mortality, are challenging to identify. LR...
Recent advances in machine learning combined with the growing availability of digitized health records offer new opportunities for improving early diagnosis of depression. An emerging body of research shows th...
Transplantation represents the optimal treatment for many patients with end-stage kidney disease. When a donor kidney is available to a waitlisted patient, clinicians responsible for the care of the potential ...
Prediction models for outcomes after orthopedic surgery provide patients with evidence-based postoperative outcome expectations. Our objectives were (1) to identify prognostic factors associated with the posto...
The kidney failure risk equation (KFRE) predicts the 2- and 5-year risk of needing kidney replacement therapy (KRT) using four risk factors — age, sex, urine albumin-to-creatinine ratio (ACR) and creatinine-ba...
Osteoporosis poses a growing healthcare challenge owing to its rising prevalence and a significant treatment gap, as patients are widely underdiagnosed and consequently undertreated, leaving them at high risk ...
A lack of biomarkers that detect drug-induced liver injury (DILI) accurately continues to hinder early- and late-stage drug development and remains a challenge in clinical practice. The Innovative Medicines In...
A previous individual participant data meta-analysis (IPD-MA) of antibiotics for adults with clinically diagnosed acute rhinosinusitis (ARS) showed a marginal overall effect of antibiotics, but was unable to i...
In a pandemic setting, it is critical to evaluate and deploy accurate diagnostic tests rapidly. This relies heavily on the sample size chosen to assess the test accuracy (e.g. sensitivity and specificity) duri...
Numerous biomarkers have been proposed for diagnosis, therapeutic, and prognosis in sepsis. Previous evaluations of the value of biomarkers for predicting mortality due to this life-threatening condition fail ...
Rapid antigen tests detecting SARS-CoV-2 were shown to be a useful tool in managing the COVID-19 pandemic. Here, we report on the results of a prospective diagnostic accuracy study of four SARS-CoV-2 rapid ant...
Group A streptococcus is found in 20–40% of cases of childhood pharyngitis; the remaining cases are viral. Streptococcal pharyngitis (“strep throat”) is usually treated with antibiotics, while these are not in...
Clinical scores help physicians to make clinical decisions, and some are recommended by health authorities for primary care use. As an increasing number of scores are becoming available, there is a need to und...
A number of recent papers have proposed methods to calculate confidence intervals and p values for net benefit used in decision curve analysis. These papers are sparse on the rationale for doing so. We aim to ass...
Prediction algorithms that quantify the expected benefit of a given treatment conditional on patient characteristics can critically inform medical decisions. Quantifying the performance of treatment benefit pr...
The performance of models for binary outcomes can be described by measures such as the concordance statistic (c-statistic, area under the curve), the discrimination slope, or the Brier score. At internal valid...
The multivariable fractional polynomial (MFP) approach combines variable selection using backward elimination with a function selection procedure (FSP) for fractional polynomial (FP) functions. It is a relativ...
The COVID-19 pandemic has a large impact worldwide and is known to particularly affect the older population. This paper outlines the protocol for external validation of prognostic models predicting mortality r...
The conventional count-based physical frailty phenotype (PFP) dichotomizes its criterion predictors—an approach that creates information loss and depends on the availability of population-derived cut-points. T...
Many potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is...
Personalized disease management informed by quantitative risk prediction has the potential to improve patient care and outcomes. The integration of risk prediction into clinical workflow should be informed by ...
While administrative health records such as national registries may be useful data sources to study the epidemiology of psoriasis, they do not generally contain information on disease severity.
Cervical cancer remains a public health problem worldwide, especially in sub-Saharan Africa. There are challenges in timely screening and diagnosis for early detection and intervention. Therefore, studies on c...
Simple blood tests can play an important role in identifying patients for cancer investigation. The current evidence base is limited almost entirely to tests used in isolation. However, recent evidence suggest...
Clinical prediction models must be appropriately validated before they can be used. While validation studies are sometimes carefully designed to match an intended population/setting of the model, it is common ...
Cardiovascular disease (CVD) is a leading cause of death among women. CVD is associated with reduced quality of life, significant treatment and management costs, and lost productivity. Estimating the risk of C...
The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known com...
Schizophrenia is a severe mental illness characterized by recurrent psychoses that typically waxes and wanes through its prodromal, acute, and chronic phases. A large amount of research on individual prognosti...
The incidence and mortality of liver cancer have been increasing in the UK in recent years. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) ...
Rectal cancer has a high prevalence. The standard of care for management of localised disease involves major surgery and/or chemoradiotherapy, but these modalities are sometimes associated with mortality and m...
The Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (DTA) provides guidance on important aspects of conducting a test accuracy systematic review. In this paper we present TOMAS-R (Template...
The severity of SARS-CoV-2 infection varies from asymptomatic state to severe respiratory failure and the clinical course is difficult to predict. The aim of the study was to develop a prognostic model to pred...
There is increasing evidence supporting the use of faecal immunochemical tests (FIT) in patients reporting symptoms associated with colorectal cancer (CRC), but most studies until now have focused on selected ...
Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven chal...
With the rise of artificial intelligence (AI) in ophthalmology, the need to define its diagnostic accuracy is increasingly important. The review aims to elucidate the diagnostic accuracy of AI algorithms in sc...
Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their...
Evaluating the accuracy of extrapulmonary tuberculosis (TB) tests is challenging due to lack of a gold standard. Latent class analysis (LCA), a statistical modeling approach, can adjust for reference tests’ im...
There is substantial interest in the adaptation and application of so-called machine learning approaches to prognostic modelling of censored time-to-event data. These methods must be compared and evaluated aga...
Clinical prediction models/scores help clinicians make optimal evidence-based decisions when caring for their patients. To critically appraise such prediction models for use in a clinical setting, essential in...
Prediction modeling studies often have methodological limitations, which may compromise model performance in new patients and settings. We aimed to examine the relation between methodological quality of model ...
In response to the global COVID-19 pandemic, many in vitro diagnostic (IVD) tests for SARS-CoV-2 have been developed. Given the urgent clinical demand, researchers must balance the desire for precise estimates...
When a predictor variable is measured in similar ways at the derivation and validation setting of a prognostic prediction model, yet both differ from the intended use of the model in practice (i.e., “predictor...
With rising cost pressures on health care systems, machine-learning (ML)-based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation o...
2023 Speed
22 days submission to first editorial decision for all manuscripts (Median)
174 days submission to accept (Median)
2023 Usage
244,669 downloads
264 Altmetric mentions