Comparing and combining stable isotope datasets from different laboratories and different years is essential for many research areas, such as isotope hydrology, greenhouse gas observations, food studies, isotope forensics, palaeo-reconstructions, etc. Data compatibility (i.e. the ability to combine data) is related to the data quality. The prerequisite for data comparability is data normalization to a common stable isotope scale (often referred to as calibration) based on reliable reference materials (RMs) with accurately assigned values and uncertainties. Still, that does not guarantee the data compatibility (mutual agreement). Albeit metrological concepts related to data compatibility and measurement uncertainty have been developed and applied to analytical chemistry in general, these concepts have not yet been fully applied to stable isotope research. This can affect daily calibrations, analytical data and, therefore, data compatibility. In addition, IRMS users often prepare different laboratory standards themselves. Thereafter, users should then understand the contemporary concepts used for assigning RM value and uncertainty, as well as the limitations and potential problems associated with RMs. The history of RMs, preparation reports and also some problems in the past provide lessons to be learned. These include the δ13C drift of LSVEC (the second anchor on the δ13C scale before 2017), revisions to the value assignment principles, the introduction of replacements for LSVEC, related disputes and the potential underestimation of uncertainties for secondary RMs. The review describes metrological concepts related to isotopic scales, RMs and calibration hierarchies and data compatibility. The main RMs and their uncertainties are reviewed through the lens of metrology concepts. Additional focus is given to the VPDB scale for δ13C and issues of scale discontinuity, which can significantly reduce data compatibility in δ13C. The given examples of value and uncertainty assignment for RMs should be viewed as an example of value and uncertainty calculation in daily practice.

OBJECTIVE: Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia.
RESULTS: From 9382 studies retrieved, 82 were included. Sixty-six publications exclusively reported eighty-four classical regression models to predict variable timing of onset of pre-eclampsia. Another six publications reported purely ML algorithms, whilst another 10 publications reported ML algorithms and classical regression models in the same sample with 8 of 10 findings that ML algorithms outperformed classical regression models. The most frequent prognostic factors were age, pre-pregnancy body mass index, chronic medical conditions, parity, prior history of pre-eclampsia, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein A. Top performing ML algorithms were random forest (area under the curve (AUC) = 0.94, 95% confidence interval (CI) 0.91-0.96) and extreme gradient boosting (AUC = 0.92, 95% CI 0.90-0.94). The competing risk model had similar performance (AUC = 0.92, 95% CI 0.91-0.92) compared with a neural network. Calibration performance was not reported in the majority of publications. ML algorithms had better performance compared to classical regression models in pre-eclampsia prediction. Random forest and boosting-type algorithms had the best prediction performance. Further research should focus on comparing ML algorithms to classical regression models using the same samples and evaluation metrics to gain insight into their performance. External validation of ML algorithms is warranted to gain insights into their generalisability.

Objective.Accelerometers are devices commonly used to measure human physical activity and sedentary time. Accelerometer capabilities and analytical techniques have evolved rapidly, making it difficult for researchers to keep track of advances and best practices for data processing and analysis. The objective of this scoping review is to determine the existing methods for analyzing accelerometer data for capturing human movement which have been validated against the criterion measure of direct observation.Approach.This scoping review searched 14 academic and 5 grey databases. Two independent raters screened by title and abstract, then full text. Data were extracted using Microsoft Excel and checked by an independent reviewer.Mainresults.The search yielded 1039 papers and the final analysis included 115 papers. A total of 71 unique accelerometer models were used across a total of 4217 participants. While all studies underwent validation from direct observation, most direct observation occurred live (55%) or using recordings (42%). Analysis techniques included machine learning (ML) approaches (22%), the use of existing cut-points (18%), receiver operating characteristic curves to determine cut-points (14%), and other strategies including regressions and non-ML algorithms (8%).Significance.ML techniques are becoming more prevalent and are often used for activity identification. Cut-point methods are still frequently used. Activity intensity is the most assessed activity outcome; however, both the analyses and outcomes assessed vary by wear location. This scoping review provides a comprehensive overview of accelerometer analysis and validation techniques using direct observation and is a useful tool for researchers using accelerometers.

Prognostic models provide an avenue to predict the risk of individual patients and support shared-decision making. Many prognostic models are published annually, and systematic reviews provide an avenue to collate the existing evidence behind prognostic models to determine whether a model demonstrates adequate predictive performance and is ready for real-world use. This article provides a brief step-by-step guide on how to conduct a systematic review and meta-analysis of prognostic model studies and how these reviews differ from systematic reviews of therapy and diagnosis.

Plantar pressure measurement systems are routinely used in sports and health applications to assess locomotion. The purpose of this review is to describe and critically discuss: (a) applications of the pressure measurement systems in sport and healthcare, (b) testing protocols and considerations for clinical gait analysis, (c) clinical recommendations for interpreting plantar pressure data, (d) calibration procedures and their accuracy, and (e) the future of pressure sensor data analysis. Rigid pressure platforms are typically used to measure plantar pressures for the assessment of foot function during standing and walking, particularly when barefoot, and are the most accurate for measuring plantar pressures. For reliable data, two step protocol prior to contacting the pressure plate is recommended. In-shoe systems are most suitable for measuring plantar pressures in the field during daily living or dynamic sporting movements as they are often wireless and can measure multiple steps. They are the most suitable equipment to assess the effects of footwear and orthotics on plantar pressures. However, they typically have lower spatial resolution and sampling frequency than platform systems. Users of pressure measurement systems need to consider the suitability of the calibration procedures for their chosen application when selecting and using a pressure measurement system. For some applications, a bespoke calibration procedure is required to improve validity and reliability of the pressure measurement system. The testing machines that are commonly used for dynamic calibration of pressure measurement systems frequently have loading rates of less than even those found in walking, so the development of testing protocols that truly measure the loading rates found in many sporting movements are required. There is clear potential for AI techniques to assist in the analysis and interpretation of plantar pressure data to enable the more complete use of pressure system data in clinical diagnoses and monitoring.

Population-adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head-to-head trials are unavailable. Three commonly used PAIC methods include matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta-regression (ML-NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user-friendly R Shiny application Shiny-MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny-MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny-MAIC application, enabling a user-friendly approach conducting MAIC for healthcare decision-making. The Shiny-MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/.

The purpose of this meta-analysis was to quantify the difference in physical activity and sleep estimates assessed via 1) movement, 2) heart rate (HR), or 3) the combination of movement and HR (MOVE+HR) compared to criterion indicators of the outcomes. Searches in four electronic databases were executed September 21-24 of 2021. Weighted mean was calculated from standardized group-level estimates of mean percent error (MPE) and mean absolute percent error (MAPE) of the proxy signal compared to the criterion measurement method for physical activity, HR, or sleep. Standardized mean difference (SMD) effect sizes between the proxy and criterion estimates were calculated for each study across all outcomes, and meta-regression analyses were conducted. Two-One-Sided-Tests method were conducted to metaanalytically evaluate the equivalence of the proxy and criterion. Thirty-nine studies (physical activity k = 29 and sleep k = 10) were identified for data extraction. Sample size weighted means for MPE were -38.0%, 7.8%, -1.4%, and -0.6% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Sample size weighted means for MAPE were 41.4%, 32.6%, 13.3%, and 10.8% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Few estimates were statistically equivalent at a SMD of 0.8. Estimates of physical activity from MOVE+HR were not statistically significantly different from estimates based on movement or HR only. For sleep, included studies based their estimates solely on the combination of MOVE+HR, so it was impossible to determine if the combination produced significantly different estimates than either method alone.

OBJECTIVE: To review cut-points calibrated and independently validated from wrist-worn ActiGraph accelerometers to measure moderate to vigorous physical activity (MVPA) and time spent sedentary (SED) in children and adolescents.
METHODS: Systematic literature review.
METHODS: Five databases were searched for relevant cut-point calibration and independent validation studies relating to wrist worn ActiGraphs in children and adolescents from inception through 30 April 2022. Extracted data included: country of publication; study name; population; device model; wear location; sampling frequency; epoch length; activity protocol; criterion method and definitions used to classify PA intensity; statistical methods for calibration; statistical methods for validation/cross-validation; and MVPA and SED outcome.
RESULTS: Fourteen calibration studies and seven independent validation studies were identified. Calibrated cut-points for MVPA vector magnitude counts ranged from 7065 to 9204 counts per minute (cpm) and 63.5 to 201 milli-gravitational units (mg). For SED, calibrated cut-points ranged from <2556 cpm to 4350 cpm and 30.8 to 48.1 mg. Classification accuracy values determined by independent validation studies varied, with kappa values ranging from 0.31 to 0.60 and area under the curve statistics ranging from 0.51 to 0.84 for MVPA and kappa values ranging from 0.31 to 0.44 and area under the curve statistics ranging from 0.70 to 0.85 for SED.
CONCLUSIONS: The results of this systematic literature review support the use of the Crouter and colleagues cut-points for the measurement of MVPA and SED for children and adolescents aged 6-12 years. Further work is required to independently validate cut-points developed in younger children and older adolescents.

OBJECTIVE: A de novo intracranial aneurysm (IA) is a second, new IA that develops in patients with IAs distant from where the initial IA was detected. This study aimed to identify risk factors for de novo IA formation and establish and externally validate a multicenter risk prediction model for de novo IAs.
METHODS: A systematic review and meta-analysis of existing de novo IA cohorts was conducted to form the derivation cohort. The risk ratios and 95% CIs of each risk factor were calculated. In addition, risk scores included in the model were calculated based on the statistically significant risk factors with their weightings. Then the model was validated in a multicenter external cohort of Chinese patients, and receiver operating characteristic and calibration curves, decision curve analysis, and Kaplan-Meier curves were used to evaluate the model.
RESULTS: Nineteen studies with 9351 patients, of whom 304 patients (3.25%) developed de novo IAs, were included in the derivation cohort. These patients developed de novo IAs at 2.5-18.5 years during a total follow-up of 3.3-18.8 years. The statistically significant risk factors were age < 60 years, female sex, smoking history, family history of IAs, multiple IAs at initial diagnosis, and initial IAs in the middle cerebral artery, with risk scores of 4, 5, 2, 6, 3, and 3, respectively. Then, a multicenter external cohort comprising 776 patients, of whom 45 patients (5.80%) developed de novo IAs, was included in the validation cohort. De novo IAs formed in these patients at a mean of 5.25 years during a mean follow-up of 6.19 years. The area under the curve of the model was 0.804, with a sensitivity of 0.667 and specificity of 0.900, at a cutoff value of 13. The calibration curve, decision curve analysis, and Kaplan-Meier curves also indicated good performance of the model.
CONCLUSIONS: This prediction model is a convenient and intuitive tool for identifying high-risk patients with de novo IAs. Reasonable use of the model can not only aid in clinical decision-making but also play a positive role in the prevention of aneurysmal subarachnoid hemorrhage to a certain extent.

The exponential growth of human population and anthropogenic activities have led to the increase of global surface water contamination especially in river, lakes and ocean. Safe and clean surface water sources are crucial to human health and well-being, aquatic ecosystem, environment and economy. Thus, water monitoring is vital to ensure minimal and controllable contamination in the water sources. The conventional surface water monitoring method involves collecting samples on site and then testing them in the laboratory, which is time-consuming and not able to provide real-time water quality data. In addition, it involves many manpower and resources, costly and lack of integration. These make surface water quality monitoring more challenging. The incorporation of Internet of Things (IoT) and smart technology has contributed to the improvement of monitoring system. There are different approaches in the development and implementation of online surface water quality monitoring system to provide real-time data collection with lower operating cost. This paper reviews the sensors and system developed for the online surface water quality monitoring system in the previous studies. The calibration and validation of the sensors, and challenges in the design and development of online surface water quality monitoring system are also discussed.