Prediction Intervals Research Articles

Robust Prediction Intervals for Valuation of Large Portfolios of Variable Annuities: A Comparative Study of Five Models

Robust Prediction Intervals for Valuation of Large Portfolios of Variable Annuities: A Comparative Study of Five Models

Beyond point forecasts: Uncertainty quantification in tensor extrapolation for relational time series data

Contemporary data sets are frequently relational in nature. In retail, for example, data sets are more granular than traditional data, often indexing individual products, outlets, or even customers, rather than aggregating them at the group level. Tensor extrapolation aims to forecast relational time series data; it combines tensor decompositions and time series extrapolation. However, previous approaches to tensor extrapolation are restricted to point forecasts and fail to quantify the uncertainty associated with the forecasts. This paper provides prediction intervals within which we expect the true future values to lie with a specified probability. Numerical experiments suggest the effectiveness of the proposed method in terms of forecast performance. Moreover, the approach proves to be up to 77 times faster than the univariate competitor. For this reason, it exerts positive influence on both inclusiveness and the environment.

A hybrid prediction interval model for short-term electric load forecast using Holt-Winters and Gate Recurrent Unit

Short-term load forecasting (STLF) is essential for power system planning and operation decision-making. Researchers in this field have devoted themselves to building point prediction models in recent years. However, traditional point forecasting treats the forecast outcome as a deterministic variable. Due to the inherent complexity, volatility, and instability of power load, deviations in traditional point forecasting methods are inevitable and significant. Considering the difficulty of accurate point prediction, interval prediction can tolerate increased uncertainty and provide more information for actual operation decision-making. In order to solve this problem, we proposed a novel hybrid model based on Holt-Winters (HW) method and gated recurrent unit (GRU) network for short-term load interval forecasting. First, considering the factors affecting the load trend, the weather type, date type, and historical load data are processed and normalized. Then, the raw data is decomposed into linear components and nonlinear residuals using a moving average (MA) filter. Next, the HW method establishes a linear prediction model to predict the linear component. Finally, an interval prediction model based on gated recurrent unit neural network is established by taking linear prediction results, nonlinear residuals, weather types, date types, and original data as inputs. In order to verify the performance of the proposed hybrid method, based on the data set provided by the 2022 "Teddy Cup" data mining challenge, taking 15-minute load data as an example, the traditional model and the advanced interval prediction model were compared. The results show that the proposed model is a high-quality method. Compared with models built by other methods, it has a higher prediction interval coverage probability and a narrower prediction interval width.

A new tool for energy conservation in operating room: The role of machine learning models in enhancing airflow control

Maintaining high ventilation rates in operating rooms (ORs) is critical to ensure the safety of healthcare professionals and patients. Yet, the significant energy consumption associated with ventilation systems is a growing global concern. While traditional approaches have predominantly focused on optimizing ventilation design, limited attention has been given to operational control strategies. This study aims to control ceiling air supply speed by exposure aerosol concentrations exhaled by the patient. It achieves this by integrating empirical data with machine learning (ML) models, effectively balancing the need for maintaining optimal ventilation conditions and ensuring a safe aerosol exposure concentration. Experimental results from a full-scale OR setup, manipulating ceiling air supply speed, respiratory flow rate, and surgical bed height, generated 81 datasets. These datasets served as the basis for assessing the performance of 11 ML models. Notably, the extra trees (ET) and multi-layer perceptron (MLP) models outperformed the others, exhibiting impressive coefficient of determination (R2) values of 0.954 and 0.904, respectively. Moreover, the models were further enhanced by assessing their robustness with Gaussian noise and interval prediction. This study presents a novel approach that holds significant relevance for the practical control of ventilation systems, particularly with regard to health and energy efficiency considerations.

Comparative analysis of single and hybrid machine learning models for daily solar radiation

This study investigates the estimation of daily solar radiation (SR) through various machine learning (ML) models, including the k-nearest neighbor algorithm (KNN), support vector regression (SVR), and random forest (RF), both individually and in combination with the wavelet transform (WT). The assessment of these models is based on meteorological data spanning three decades (1981–2010) from the province of Kütahya in Türkiye. To address the inherent uncertainty in these data-driven models, the quantile regression method is employed for uncertainty analysis. Statistical metrics, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), mean prediction interval (MPI), and prediction interval coverage probability (PICP), are utilized to evaluate the effectiveness and uncertainties of the models. The SVR and KNN models exhibit comparable performances concerning both predictive accuracy and uncertainty levels. However, hybrid models, such as KNN-WT, RF-WT, and SVR-WT, display enhanced accuracy compared to individual ML models, as indicated by statistical performance criteria. Notably, the SVR-WT model, incorporating inputs such as sunshine duration, air temperature, wind speed, and relative humidity, outperforms other models in terms of RMSE (2.174 MJ/m2), MAE (1.721 MJ/m2), R2 (0.923), MPI (28.55), and PICP (0.80) for the testing dataset. In conclusion, the integration of WT significantly improves the performance of ML models, providing valuable insights for the design and operation of solar energy systems, where precise daily SR estimation is critical for optimal and cost-efficient operation.

Prediction intervals for carbon dioxide emissions in china by extreme learning machine ensemble based on particle swarm optimization

<p>The emission of carbon dioxide is the major cause of the greenhouse effect, which has a negative impact on human survival and sustainable economic development; therefore, it is very significant for discovering the underlying influential factors of carbon dioxide emissions. In this paper, an extreme learning machine ensemble based on particle swarm optimization approach(PSO-ELMEnsemble) are applied to predict the emission of carbon dioxide, which provide estimated values as well as the corresponding reliability. In addition, the particle swarm optimization approach is used to optimize the connection weights of extreme learning machine. The performance of the proposed PSO-ELMEnsemble is experimentally validated by carbon dioxide emissions data during the period 1978-2016 by using state-of-the-art comparative method, where the proposed approach outperforms the others in achieving the best trade-off between accuracy and simplicity.</p>

Computation of prediction intervals for forest aboveground biomass predictions using generalized linear models in a large-extent boreal forest region

Abstract Remotely sensed data have an important application for estimation of forest variables, e.g. height, volume, and aboveground biomass (AGB). The increased use of remotely sensed data implemented along with model-based inference has shown improved efficiency in prediction and mapping of such forest variables. In this study, plot-level airborne laser scanning data and Swedish National Forest Inventory field reference data were used to predict AGB using generalized linear models (GLMs) assuming Gamma and Tweedie distributions for the field observed AGB. The GLMs were selected considering the convenience of not correcting transformation bias as it is required in other regression models with transformed response variable. To overcome the challenge in providing reliable uncertainty estimates for the estimated forest variable map products at individual pixel-scale, we focused on computing 95% prediction intervals (PIs) for Gamma and Tweedie GLMs with a square root link function. The relative uncertainties were computed as the ratio between the half-width of the PIs and the predicted AGBs. The AGB-airborne laser scanning models were developed with root mean square error values of 22.6 Mgha−1 (26%) and 21.7 Mgha−1 (25%), respectively, for the Gamma and Tweedie GLMs. Two methods were applied to compute PIs for the Gamma GLM, one using the R package ‘ciTools’ and another derived through asymptotic theory. It was found that the 95% PIs computed using ‘ciTools’ had the most accurate coverage probability in comparison to the other method. An extended version of these PIs was also utilized for the Tweedie GLMs. The range of PIs associated with the prediction of AGB were narrower for lower predicted AGB values compared with the length of higher predicted AGB values. Comparing the two fitted models, the Gamma GLM showed lower relative uncertainties for the lower range of predicted AGBs, whereas the Tweedie GLM showed lower relative uncertainties for the higher range of predicted AGBs. Overall, the Tweedie GLM provided a better model fit for AGB predictions.

Impact of Demographic Developments and PCV13 Vaccination on the Future Burden of Pneumococcal Diseases in Germany—An Integrated Probabilistic Differential Equation Approach

Streptococcus pneumonia is the primary cause of morbidity and mortality in infants and children globally. Invasive pneumococcal disease (IPD) incidence is affected by various risk factors such as age and comorbidities. Additionally, this bacterium is a major cause of community-acquired pneumonia (CAP), leading to higher rates of hospitalization, especially among older adults. Vaccination with pneumococcal conjugate vaccines (PCVs) has proven effective, but the demographic transition in Germany poses a challenge. This study introduces a novel stochastic approach by integrating a population forecast model into a transmission dynamic model to investigate the future burden of pneumococcal diseases in three age groups (0–4, 5–59, and 60 and older). Our simulations, presented through mean predictions and 75% prediction intervals, indicate that implementing PCV13 (13-valent pneumococcal conjugate vaccine) until the year 2050 results in reduced cases of IPD and CAP in all age groups compared to scenarios without infant vaccination. However, cases with non-vaccine serotypes may persist at higher levels compared to scenarios without infant vaccination. Consequently, there may be a need for improvement in the current national vaccine policy, such as implementing the use of higher-valent PCVs and strengthening adult vaccination uptake.

Critical slowing down characteristics of acoustic emission for fracture instability of sandstone down-slope rock bridge under cyclic wetting and drying

Down-slope rock bridge in slopes and dangerous rock masses are often affected by cyclic wetting and drying, leading to severe geological disasters. In order to investigate the deterioration mechanism, deformation failure mode and instability precursor information of sandstone down-slope rock bridge under different cyclic wetting and drying, uniaxial compression tests combined with acoustic emission (AE) tests were conducted. The failure of the specimen experienced four stages. As the number of the cyclic wetting and drying increased, the specimen’s uniaxial compressive strength and elastic modulus decreased, and the failure mode transitioned from tensile to shear. Utilizing the critical slowing down theory, the variance and autocorrelation coefficient curves of AE multi-parameters were analyzed. The abrupt change of RA value, RA/AF value, and b value at the crack damage stress point and the abrupt change of the peak frequency lagging behind the crack damage stress point can be used as the precursors of instability. The instability precursor identified based on the peak frequency variance is closer to the specimen’s peak stress point and can be used for short-term failure prediction. The autocorrelation coefficient curve of AE multi-parameters aligned with the variance curve, indicating that with an increased number of cyclic wetting and drying, the instability precursor appeared earlier, leading to a larger failure prediction time interval.

Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis

Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA). Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed. Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively. We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation. The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.

Quantifying the association of sperm DNA fragmentation with assisted reproductive technology outcomes: An umbrella review.

Systematic reviews and meta-analyses are instrumental in shaping clinical practice. However, their findings can sometimes be marred by discrepancies and potential biases, thereby diluting the strength of the evidence presented. Umbrella reviews serve to comprehensively assess and synthesise these reviews, offering a clearer insight into the quality of the evidence presented. In the context of the relationship between sperm DNA fragmentation (SDF) and assisted conception outcomes, there is a divergence in the literature. Some reviews suggest a clear cause-and-effect linkage, whereas others present conflicting or inconclusive results. In this umbrella review we aimed to synthesise the evidence collated in systematic reviews and meta-analyses summarising the association of SDF with assisted reproductive technology (ART) outcomes. After preregistration (https://doi.org/10.17605/OSF.IO/6JHDP), we performed a comprehensive search of the PubMed, Scopus, Cochrane Library, Web of Science and Embase databases. We conducted a search for systematic reviews on the association between SDF and ART without any restrictions on language or publication date. Systematic reviews and meta-analyses assessing the association between SDF and ART outcomes were eligible. We assessed the quality of the included reviews using AMSTAR 2 and ROBIS, and determined the degree of overlap of primary studies between reviews estimating the corrected covered area (CCA), adjusted for structural missingness. We evaluated the most recent reviews assessing the association of SDF with live birth, pregnancy, miscarriage, implantation, blastulation and fertilisation. The synthesis of evidence was harmonised across all included quantitative syntheses, re-estimating the odds ratio (eOR) in random-effects meta-analyses with 95% confidence intervals (95% CIs) and 95% prediction intervals (95% PIs). We categorised the evidence strength into convincing, highly suggestive, suggestive, weak or nonsignificant, according to the meta-analysis re-estimated P-value, total sample size, I2 statistic for heterogeneity, small study effect, excess significance bias and the largest study significance. We initially captured and screened 49 332 records. After excluding duplicate and ineligible articles, 22 systematic reviews, 15 of which were meta-analyses, were selected. The 22 reviews showed a moderate degree of overlap (adjusted CCA 9.2%) in their included studies (overall n = 428, with 180 unique studies). The 15 meta-analyses exhibited a high degree of overlap (adjusted CCA = 13.6%) in their included studies (overall n = 274, with 118 unique studies). AMSTAR 2 categorised the quality of evidence in 18 reviews as critically low and the quality of evidence in four reviews as low. ROBIS categorised all the reviews as having a high risk of bias. The re-estimated results showed that the association of SDF with live birth was weak in one and nonsignificant in four meta-analyses. Similarly, the association of SDF with pregnancy, miscarriage, implantation, blastulation and fertilisation was also weak or nonsignificant. The association of high SDF with different ART outcomes was also weak or nonsignificant for different interventions (IVF, ICSI and IUI) and tests. This umbrella review did not find convincing or suggestive evidence linking SDF with ART outcomes. Caution should be exercised in making any claims, policies or recommendations concerning SDF.

Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021.

BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.ResultsThe model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.ConclusionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.

Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM

Accurate prediction of wind speed is significant importance in various applications such as renewable energy management, weather prediction, and aviation safety. However, more papers only focus on the time series of wind speed, ignoring the impact of other factors on wind speed, which will reduce the effectiveness of wind speed prediction. Therefore, an innovative methodology is suggested for both point and interval forecasts of multivariate wind speed time series using a Dual-layer Long Short-Term Memory (NVDL) in this paper. The proposed multivariate forecasting model takes into account the dependencies and correlations among different variables, which are essential for capturing the complex dynamics of wind speed variations. The first layer of the Dual-layer LSTM is responsible for capturing the temporal dependencies within each variable individually, while the second layer captures the interdependencies among the variables. By incorporating this dual-layer architecture, our model effectively captures the complex spatiotemporal patterns present for multivariate wind speed information. The results obtained from both interval and point prediction demonstrate that the proposed methodology outperforms all comparative models in the precision and stability of wind speed forecasting. Therefore, the proposed forecasting methodology, characterized by minimal prediction errors and exceptional generalization ability, can be a reliable tool for smart grid programming.

Enhanced cholera surveillance to improve vaccination campaign efficiency

Systematic testing for Vibrio cholerae O1 is rare, which means that the world’s limited supply of oral cholera vaccines (OCVs) may not be delivered to areas with the highest true cholera burden. Here we used a phenomenological model with subnational geographic targeting and fine-scale vaccine effects to model how expanding V. cholerae testing affected impact and cost-effectiveness for preventive vaccination campaigns across different bacteriological confirmation and vaccine targeting assumptions in 35 African countries. Systematic testing followed by OCV targeting based on confirmed cholera yielded higher efficiency and cost-effectiveness and slightly fewer averted cases than status quo scenarios targeting suspected cholera. Targeting vaccine to populations with an annual incidence rate greater than 10 per 10,000, the testing scenario averted 10.8 (95% prediction interval (PI) 9.4–12.6) cases per 1,000 fully vaccinated persons while the status quo scenario averted 6.9 (95% PI 6.0–7.8) cases per 1,000 fully vaccinated persons. In the testing scenario, testing costs increased by US$31 (95% PI 25–39) while vaccination costs reduced by US$248 (95% PI 176–326) per averted case compared to the status quo. Introduction of systematic testing into cholera surveillance could improve efficiency and reach of global OCV supply for preventive vaccination.

Prevalence and assessment tools of cancer-related cognitive impairment in lung cancer survivors: a systematic review and proportional meta-analysis

PurposeCancer-related cognitive impairment (CRCI) is a significant risk factor influencing the quality of life in lung cancer survivors. No absolute assessment tool has been confirmed to assess CRCI in lung cancer survivors. This review was undertaken to pool the overall prevalence of CRCI and to summarize the assessment tools in assessing CRCI among lung cancer survivors.MethodsPubMed, Cochrane Library, Embase, CINAHL, and CNKI were searched to retrieve articles reported CRCI prevalence. Summary prevalence estimates were pooled using a random effects model, along with corresponding 95% prediction intervals (PIs). The Freeman-Tukey double arcsine transformation of proportions was incorporated in the analysis. Additionally, subgroup analysis, meta-regression, and leave-one-out analysis were performed.ResultsA total of 12 studies, involving 1934 survivors, were included in the review. All of these studies were found to have a low risk of bias in terms of their methodological quality. Four studies (33.3%) utilized the International Cognition and Cancer Task Force (ICCTF) criteria to identify CRCI through neuropsychological tests. The pooled prevalence rate of CRCI was found to be 26% (95% PI, 16–37%), I2 = 95.97%. The region in which the studies were conducted was identified as a significant factor contributing to this heterogeneity (p = 0.013). No indication of small-study effects was found (Egger’s test: p = 0.9191).ConclusionThis review provides an overview of CRCI prevalence and assessment tools in lung cancer survivors. The findings can serve as epidemiological evidence to enhance clinicians’ and researchers’ understanding of early detection and assessment.

A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction

It is crucial to predict the wind speed for the utilization of renewable wind energy and the operation of transmission lines with increased capacity. The intermittency and stochastic fluctuations of wind speed pose a significant challenge for the high-quality wind speed prediction, and a novel wind speed interval prediction (WSIP) model is constructed in this study by employing the residual estimation (RE)-oriented dynamic spatio-temporal graph convolutional network (DSTGCN) approach. Firstly, a dynamic adjacency matrix is designed to obtain time-varying global spatial weight allocations among each wind speed node. Then, the spatio-temporal features are extracted by using gated recurrent units (GRUs) and GCNs to construct the wind speed graph networks. Moreover, the RE-oriented strategy incorporating the pinball loss is designed to provide a guidance the parameter training of the constructed model, thus eliminating the quantile crossings problem. As a result, the deterministic point prediction of the wind speed is expanded to the quantile-based probabilistic interval prediction. Finally, the experimental results are presented to demonstrate the validity and superiority of proposed scheme in both qualitative and quantitative performance.

Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned

In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tools for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases (early, exponential growth, peak, and decline) during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.

Meta-analysis prediction intervals are under reported in sport and exercise medicine.

Prediction intervals are a useful measure of uncertainty for meta-analyses that capture the likely effect size of a new (similar) study based on the included studies. In comparison, confidence intervals reflect the uncertainty around the point estimate but provide an incomplete summary of the underlying heterogeneity in the meta-analysis. This study aimed to estimate (i) the proportion of meta-analysis studies that report a prediction interval in sports medicine; and (ii) the proportion of studies with a discrepancy between the reported confidence interval and a calculated prediction interval. We screened, at random, 1500 meta-analysis studies published between 2012 and 2022 in highly ranked sports medicine and medical journals. Articles that used a random effect meta-analysis model were included in the study. We randomly selected one meta-analysis from each article to extract data from, which included the number of estimates, the pooled effect, and the confidence and prediction interval. Of the 1500 articles screened, 866 (514 from sports medicine) used a random effect model. The probability of a prediction interval being reported in sports medicine was 1.7% (95% CI = 0.9%, 3.3%). In medicine the probability was 3.9% (95% CI = 2.4%, 6.6%). A prediction interval was able to be calculated for 220 sports medicine studies. For 60% of these studies, there was a discrepancy in study findings between the reported confidence interval and the calculated prediction interval. Prediction intervals were 3.4 times wider than confidence intervals. Very few meta-analyses report prediction intervals and hence are prone to missing the impact of between-study heterogeneity on the overall conclusions. The widespread misinterpretation of random effect meta-analyses could mean that potentially harmful treatments, or those lacking a sufficient evidence base, are being used in practice. Authors, reviewers, and editors should be aware of the importance of prediction intervals.

Dipsticks and point-of-care Microscopy to reduce antibiotic use in women with an uncomplicated Urinary Tract Infection (MicUTI): protocol of a randomised controlled pilot trial in primary care

IntroductionUncomplicated urinary tract infections (uUTIs) in women are common infections encountered in primary care. Evidence suggests that rapid point-of-care tests (POCTs) to detect bacteria and erythrocytes in urine at presentation...

Validity of spatiotemporal and ground reaction force estimates during resisted sprinting with a motorized loading device.

We aimed to examine the validity of estimating spatiotemporal and ground reaction force (GRF) parameters during resisted sprinting using a robotic loading device (1080 Sprint). Twelve male athletes (age: 20.9 ± 2.2 years; height: 174.6 ± 4.2 cm; weight: 69.4 ± 6.1 kg; means ± SDs) performed maximal resisted sprinting with three different loads using the device. The step frequency and length and step-averaged velocity, anteroposterior GRF (Fap ), and the ratio of Fap to resultant GRF (RF) were estimated using the velocity and towing force data measured using the device. Simultaneously, the corresponding values were measured using a 50-m force plate system. The proportional and fixed biases of the estimated values against those measured using the force plate system were determined using ordinary least product (OLP) regression analysis. Proportional and fixed biases were observed for most variables. However, the proportional bias was small or negligible except for the step frequency. Conversely, the fixed bias was small for step-averaged velocity (0.11 m/s) and step length (0.04 m), whereas it was large for step frequency (0.54 step/s), Fap (16N), and RF (2.22%). For all variables except step frequency, the prediction intervals in the OLP regression dramatically decreased when the corresponding values were smoothed using a two-step moving average. These results indicate that by using the velocity and force data recorded in the loading device, most of the spatiotemporal and GRF variables during resisted sprinting can be estimated with some correction of the fixed bias and data smoothing using the two-step moving average.