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This study systematically reviewed and synthesized the literature on psychological and clinical outcomes of receiving a variant of uncertain significance (VUS) from multigene panel testing or genomic sequencing. MEDLINE and EMBASE were searched. Two reviewers screened studies and extracted data. Data were synthesized through meta-analysis and meta-aggregation. The search identified 4539 unique studies and 15 were included in the review. Patients with VUS reported higher genetic test–specific concerns on the Multidimensional Impact of Cancer Risk Assessment (MICRA) scale than patients with negative results (mean difference 3.73 [95% CI 0.80 to 6.66] P = 0.0126), and lower than patients with positive results (mean difference −7.01 [95% CI −11.31 to −2.71], P = 0.0014). Patients with VUS and patients with negative results were similarly likely to have a change in their clinical management (OR 1.41 [95% CI 0.90 to 2.21], P = 0.182), and less likely to have a change in management than patients with positive results (OR 0.09 [95% CI 0.05 to 0.19], P < 0.0001). Factors that contributed to how patients responded to their VUS included their interpretation of the result and their health-care provider’s counseling and recommendations. Review findings suggest there may be a need for practice guidelines or clinical decision support tools for VUS disclosure and management.

Exome sequencing and genome sequencing have the potential to improve clinical utility for patients undergoing genetic investigations. However, evidence of clinical utility is limited to pediatric populations; we aimed to fill this gap by conducting a systematic review of the literature on the clinical utility of exome/genome sequencing across disease indications in pediatric and adult populations. MEDLINE, EMBASE and Cochrane Library were searched between 2016 and 2020. Quantitative studies evaluating diagnostic yield were included; other measures of clinical utility such as changes to clinical management were documented if reported. Two reviewers screened, extracted data, and appraised risk of bias. Fifty studies met our inclusion criteria. All studies reported diagnostic yield, which ranged from 3 to 70%, with higher range of yields reported for neurological indications and acute illness ranging from 22 to 68% and 37–70%, respectively. Diagnoses triggered a range of clinical management changes including surveillance, reproductive-risk counseling, and identifying at-risk relatives in 4–100% of patients, with higher frequencies reported for acute illness ranging from 67 to 95%. The frequency of variants of uncertain significance ranged from 5 to 85% across studies with a potential trend of decreasing frequency over time and higher rates identified in patients of non-European ancestry. This review provides evidence for a higher range of diagnostic yield of exome/genome sequencing compared to standard genetic tests, particularly in neurological and acute indications. However, we identified significant heterogeneity in study procedures and outcomes, precluding a meaningful meta-analysis and certainty in the evidence available for decision-making. Future research that incorporates a comprehensive and consistent approach in capturing clinical utility of exome/genome sequencing across broader ancestral groups is necessary to improve diagnostic accuracy and yield and allow for analysis of trends over time.Prospero registration CRD42019094101.

PurposeWe aimed to provide clinicians with introductory guidance for interpreting and assessing confidence in on Network meta-analysis (NMA) results.MethodsWe reviewed current literature on NMA and summarized key points.ResultsNetwork meta-analysis (NMA) is a statistical method for comparing the efficacy of three or more interventions simultaneously in a single analysis by synthesizing both direct and indirect evidence across a network of randomized clinical trials. It has become increasingly popular in healthcare, since direct evidence (head-to-head randomized clinical trials) are not always available. NMA methods are categorized as either Bayesian or frequentist, and while the two mostly provide similar results, the two approaches are theoretically different and require different interpretations of the results.ConclusionsWe recommend a careful approach to interpreting NMA results and the validity of an NMA depends on its underlying statistical assumptions and the quality of the evidence used in the NMA.

Background Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan–Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves. Methods The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm’s performance was also evaluated by Bland–Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves. Results The automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement. Conclusions The algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/ .

MRI-guided laser interstitial thermal therapy (MRgLITT) is a new minimally invasive treatment for temporal lobe epilepsy (TLE), with limited effectiveness data. It is unknown if the cost savings associated with shorter hospitalization could offset the high equipment cost of MRgLITT. We examined the cost-utility of MRgLITT versus surgery for TLE from healthcare payer perspective, and the value of additional research to inform policy decision on MRgLITT. We developed a microsimulation model to evaluate quality adjusted life years (QALYs), costs, and incremental cost-effectiveness ratio (ICER) of MRgLITT versus surgery in TLE, assuming life-time horizon and 1.5% discount rate. Model inputs were derived from the literature. We conducted threshold and sensitivity analyses to examine parameter uncertainties, and expected value of partial perfect information analyses to evaluate the expected monetary benefit of eliminating uncertainty on probabilities associated with MRgLITT. MRgLITT yielded 0.08 more QALYs and cost $7,821 higher than surgery, with ICER of $94,350/QALY. Influential parameters that could change model outcomes include probabilities of becoming seizure-free from disabling seizures state and returning to disabling seizures from seizure-free state 5 years after surgery and MRgLITT, cost of MRgLITT disposable equipment, and utilities of disabling seizures and seizure-free states of surgery and MRgLITT. The cost-effectiveness acceptability curve showed surgery was preferred in more than 50% of iterations. The expected monetary benefit of eliminating uncertainty for probabilities associated with MRgLITT was higher than for utilities associated with MRgLITT. MRgLITT resulted in more QALYs gained and higher costs compared to surgery in the base-case. The model was sensitive to variations in the cost of MRgLITT disposable equipment. There is value in conducting more research to reduce uncertainty on the probabilities and utilities of MRgLITT, but priority should be given to research focusing on improving the precision of estimates on effectiveness of MRgLITT.

BackgroundParents play a pivotal role in shaping their children’s food environment and eating behaviours. Involving parents in interventions designed to promote nutritional outcomes such as dietary intake in children has been shown to improve parental feeding practices. However, it remains unclear how such interventions influence children’s eating behaviour outcomes. This protocol describes the methods of a systematic review evaluating the effectiveness of interventions involving parents in improving the eating behaviours of healthy children aged 0–12 years.Methods and analysisElectronic databases including MEDLINE, EMBASE, CENTRAL, APA PsycINFO, CINAHL, Scopus and Web of Science will be searched from inception to September 2025. A search strategy is developed to identify randomised controlled trials directly involving parents and reporting eating behaviours in children as either primary or secondary outcomes. Two independent reviewers will screen identified records and extract data on study, participant and intervention characteristics. Study results relevant to our primary and secondary outcomes will also be extracted using a prepiloted standardised data extraction form. We will use the Revised Cochrane Risk of Bias tool (RoB2) and Grading of Recommendations Assessment, Development and Evaluation approach to assess risk of bias and certainty of evidence, respectively. Where possible, meta‐analysis using random‐effects models will be performed; otherwise a qualitative summary will be provided.Ethics and disseminationEthics approval is not required for this study as no primary data will be collected. The findings will provide valuable insights for stakeholders to inform and optimise public health policies and practices aimed at empowering families to promote healthy eating behaviours early in childhood. The results will be submitted for publication in a peer-reviewed journal.PROSPERO registration numberCRD420251076540.

Clinical trials require participation of numerous patients, enormous research resources and substantial public funding. Time-consuming trials lead to delayed implementation of beneficial interventions and to reduced benefit to patients. This manuscript discusses two methods for the allocation of research resources and reviews a framework for prioritisation and design of clinical trials. The traditional error-driven approach of clinical trial design controls for type I and II errors. However, controlling for those statistical errors has limited relevance to policy makers. Therefore, this error-driven approach can be inefficient, waste research resources and lead to research with limited impact on daily practice. The novel value-driven approach assesses the currently available evidence and focuses on designing clinical trials that directly inform policy and treatment decisions. Estimating the net value of collecting further information, prior to undertaking a trial, informs a decision maker whether a clinical or health policy decision can be made with current information or if collection of extra evidence is justified. Additionally, estimating the net value of new information guides study design, data collection choices, and sample size estimation. The value-driven approach ensures the efficient use of research resources, reduces unnecessary burden to trial participants, and accelerates implementation of beneficial healthcare interventions.

Preterm birth (PTB) affects ~10% of births worldwide; however, most literature focused on short-term clinical outcomes, with much less focus on long-term socioeconomic outcomes after PTB. We examined associations between PTB and individuals' income, employment, and educational outcomes during early adulthood. We conducted a population-level matched cohort study including all live births in Canada between 1990 and 1996, followed until 2018. Outcomes included Employment income per year in 2018 CAD and employment between ages 18 and 28 years, postsecondary education enrollment (18-22 years), and maximum educational attainment at age 22-27 years. Mean differences and risk ratios (RR) and differences (RD) were estimated using generalized estimating equation regression models for economic outcomes and multinomial logistic regression models for educational outcomes. Of 2.4 million births, 7% were born preterm (0.3%, 0.6%, 0.8%, and 5.4% born extremely preterm (24-27 weeks), very preterm (28-31 weeks), moderately preterm (32-33 weeks), and late preterm (34-36 weeks) respectively). After matching on baseline characteristics (e.g., sex, province of birth, and parental demographics) and adjusting for age and period effects, preterm-born individuals, on average, had $958 CAD less employment income per year (95% CI: $854-$1062), 6% lower income per year, than term-born individuals, and were 2.13% less likely to be employed (1.98-2.29%). PTB was also negatively associated with university enrollment (RR 0.93 (0.91-0.94) and graduation with a university degree (RR 0.95 (0.94-0.97)). Mean income differences for those born 24-27 weeks were -$5,463 CAD per year (17% lower), and adjusted RR were 0.55 for university enrollment and graduation. In this population-based study, preterm birth was associated with lower economic and educational achievements at least until the late twenties. The associations were stronger with decreasing GA at birth. Policymakers, clinicians, and parents should be aware that the socioeconomic impact of PTB is not limited to the early neonatal period but extends into adulthood.

Introduction Extrapolation of time-to-event data from clinical trials is commonly used in decision models for health technology assessment (HTA). The objective of this study was to assess performance of standard parametric survival analysis techniques for extrapolation of time-to-event data for a single event from clinical trials with limited data due to small samples or short follow-up. Methods Simulated populations with 50,000 individuals were generated with an exponential hazard rate for the event of interest. A scenario consisted of 5000 repetitions with six sample size groups (30–500 patients) artificially censored after every 10% of events observed. Goodness-of-fit statistics (AIC, BIC) were used to determine the best-fitting among standard parametric distributions (exponential, Weibull, log-normal, log-logistic, generalized gamma, Gompertz). Median survival, one-year survival probability, time horizon (1% survival time, or 99th percentile of survival distribution) and restricted mean survival time (RMST) were compared to population values to assess coverage and error (e.g., mean absolute percentage error). Results The true exponential distribution was correctly identified using goodness-of-fit according to BIC more frequently compared to AIC (average 92% vs 68%). Under-coverage and large errors were observed for all outcomes when distributions were specified by AIC and for time horizon and RMST with BIC. Error in point estimates were found to be strongly associated with sample size and completeness of follow-up. Small samples produced larger average error, even with complete follow-up, than large samples with short follow-up. Correctly specifying the event distribution reduced magnitude of error in larger samples but not in smaller samples. Conclusions Limited clinical data from small samples, or short follow-up of large samples, produce large error in estimates relevant to HTA regardless of whether the correct distribution is specified. The associated uncertainty in estimated parameters may not capture the true population values. Decision models that base lifetime time horizon on the model’s extrapolated output are not likely to reliably estimate mean survival or its uncertainty. For data with an exponential event distribution, BIC more reliably identified the true distribution than AIC. These findings have important implications for health decision modelling and HTA of novel therapies seeking approval with limited evidence.

Survival rates for pediatric acute lymphoblastic leukemia (pALL) have improved dramatically; relapsed/refractory (r/r) acute lymphoblastic leukemia (ALL) remains challenging. Immunotherapies are rapidly evolving treatments for r/r ALL with limited cost‐effectiveness data. This study identifies existing economic evaluations of immunotherapy in pALL and summarizes cost‐effectiveness. Medline, Embase, and other databases were searched from inception to October 2022. Cost‐effectiveness analyses evaluating immunotherapy in pALL were included. Costs reported in 2021 USD. Of 2960 studies, 11 met inclusion criteria. Tisagenlecleucel was compared to standard of care, clofarabine monotherapy, clofarabine combination therapy, or blinatumomab. No studies have evaluated blinatumomab or inotuzumab ozogamicin. Six studies found tisagenlecleucel to be cost‐effective, five of which were supported by Novartis. Four found that it had the potential to be cost‐effective, and one found that it was not cost‐effective. The cost‐effectiveness of tisagenlecleucel was highly dependent on list price and cure rates. This study can inform the use of tisagenlecleucel in pALL. This study identifies existing economic evaluations of immunotherapy in pALL and summarizes cost‐effectiveness. Six studies found tisagenlecleucel to be cost‐effective, five of which were supported by Novartis. Cost‐effectiveness of tisagenlecleucel was highly dependent on list price and cure rates. This study can inform the use of tisagenlecleucel in pALL. .