Bias Adjustment Research Articles

Improved wind power assessments by bias adjusted reanalysed data with applications near Morocco's coast.

The wind energy requirements for high temporal and spatial observations, unavailable in many countries, are met by reanalysed wind data, which are increasingly used in wind power assessments. However, these data also include unrealistic features, making their validation compulsory. The validation analysis of ERA5 and MERRA-2 wind data, two of the most successful wind reanalyses, with observations near Morocco's coast, highlighted the existence of bias in both datasets, and other shortcomings. The study was conducted at eight selected sites in the period 2011-2020 using linear and probability density function based statistical tests. The local ERA5-BA and MERRA-2 BA velocity fields were created using Bias Adjustment (BA) Techniques. Only part of the observed and modelled datasets (training period) were used to identify the transfer function. The BA fields were verified against observations during the remaining period (verification period). They outperformed their respective non-adjusted fields and also the WFDE5 (the global bias-corrected ERA5 wind field) in the wind potential assessments, represented by capacity factor and low-wind day. At every location, the relative errors in the latter's estimates using ERA5-BA were lowered to less than 10 %, and the same was true for MERRA-2-BA. Thus, an observational dataset of reduced length can help overcome some reanalysed wind data limitations for wind power estimations. Additionally, this methodology can be applied to the correction of each reanalysis update.

Non-Participation in a Digital Health Intervention Study Among Older Adults: Uneven Involvement, Biased Outcomes and the Effect of Weighting.

The involvement of older adults in research on digital health is uneven with respect to e.g. age, gender, health status, and digital skills. However, little is known regarding the impact of the uneven involvement of older adults in digital health research on researched outcomes. This study helps to fill this knowledge gap and identifies the effects of uneven involvement of older adults in digital health research on researched outcomes, and also develops a correction for this. Data are retrieved from a digital health intervention for postoperative monitoring of people who underwent day surgery in Sweden. Based on field information on the recruitment process and researched outcomes for the intervention, this study (1) tested intervention effects by using two standard unweighted procedures in a sample of 281 individuals aged 50 years or older, and then (2) used the information on participants, non-participants and their respective probabilities to be involved in the intervention study to perform a weighting of the intervention effects for each step of selection and for the study group membership. The intervention effects were found to be overestimated due to overrepresentation of groups which gained from receiving the intervention. No intervention effects were found after adjustment for participation bias. Selective participation of older adults in digital health research biases research outcomes and can lead to overestimation of intervention effects. Weighting allows researchers to correct and describe the impact of selective participation on researched outcomes.

Quantifying Shape and Texture Biases for Enhancing Transfer Learning in Convolutional Neural Networks

Neural networks have inductive biases owing to the assumptions associated with the selected learning algorithm, datasets, and network structure. Specifically, convolutional neural networks (CNNs) are known for their tendency to exhibit textural biases. This bias is closely related to image classification accuracy. Aligning the model’s bias with the dataset’s bias can significantly enhance performance in transfer learning, leading to more efficient learning. This study aims to quantitatively demonstrate that increasing shape bias within the network by varying kernel sizes and dilation rates improves accuracy on shape-dominant data and enables efficient learning with less data. Furthermore, we propose a novel method for quantitatively evaluating the balance between texture bias and shape bias. This method enables efficient learning by aligning the biases of the transfer learning dataset with those of the model. Systematically adjusting these biases allows CNNs to better fit data with specific biases. Compared to the original model, an accuracy improvement of up to 9.9% was observed. Our findings underscore the critical role of bias adjustment in CNN design, contributing to developing more efficient and effective image classification models.

Changes in Reader Performance During Sequential Reading of Breast Cancer Screening Digital Breast Tomosynthesis Examinations.

Background Studies suggest that readers experience perceptual adaptation when interpreting batched screening mammograms, which may serve as a mechanism for improved performance. Purpose To analyze clinical digital breast tomosynthesis (DBT) screening data to evaluate changes in reader performance during sequential batch reading. Materials and Methods This observational retrospective study used data from the radiology information system collected for screening DBT examinations performed from January 2018 to December 2019. The reference standard was established based on pathology results, if applicable, or imaging findings at 1-year follow-up. Examinations were aggregated into batches, defined as a sequence of cases for a given reader with differences in interexamination time of 10 minutes or less. Mixed-effect models were used to evaluate performance and timing across batch positions. Statistical adjustments accounted for potential confounders, including patient characteristics (age, breast density), reading factors (day of week, time of day), and between-reader heterogeneity. Results The dataset included 121 652 examinations (median patient age, 61 years [IQR, 53-69 years]), including 1081 cancers interpreted by 15 radiologists. Unadjusted false-positive rates decreased from an averaged 15.5% at the first within-batch examination to 10.5% after three sequentially read examinations (P < .001), without a significant change in sensitivity (82.6% vs 84.2%; P = .15). The interpretation time consistently decreased, achieving a substantial reduction in longer batches (average 2.8 to 2.2 minutes for noncancer examinations; P < .001). Adjustment for sampling bias and confounders (patient age, breast density, day of week, time of day; all P < .005) reduced the effect on false-positive rates (11.5% to 9.4% after three examinations; P < .001) and interpretation time (3.2 to 2.7 minutes for noncancer examinations; P < .001). Conclusion Radiologists showed improved recall rates and interpretation times without change in sensitivity over the course of batch reading of DBT screening examinations. © RSNA, 2024 See also the editorial by Iima and Satake in this issue.

A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation

Hydrological studies depend heavily on environmental variables, such as precipitation and resulting runoff, which exhibit highly seasonal and intermittent behaviour in semiarid basins. In these basins, the use of traditional methods to adjust biases in time series projections can lead to inaccurate results regarding the impacts of climate change. This study introduces a non-stationary bias adjustment methodology (NS) specifically designed for environmental variables characterized by sporadic events and substantial intensity variability, such as precipitation. The methodology involves establishing a probability threshold to adapt the occurrence of precipitation events and utilizes a quantile mapping method based on a non-stationary theoretical and parametric distribution to adjust biases associated with precipitation intensity. The NS method is applied to daily precipitation projections from seven regional climatic models under the RCP 8.5 scenario spanning 2006–2100, alongside historical concurrent data from 1970 to 2005. The present method is compared to the widely used quantile delta mapping approach (QDM), revealing significant differences in performance related to the distribution of precipitation events throughout the year and the behaviour of mean and extreme intensity values. Both approaches show reliable performance, with root mean square errors between the empirical distribution functions of the corrected hindcast time series and the observations being lower than or close to 1 mm for the percentiles smaller than 50th. The error increases with the percentiles, particularly at stations located at higher altitudes. In these locations, QDM doubles or triples the error obtained with the non-stationary approach for percentiles higher than 75th, reaching up to 34 mm. The proposed methodology demonstrates promising potential in reducing uncertainties associated with systematic errors in inter-annual precipitation variability. It is a first step to jointly apply a similar approach to all involved driving variables. Such methods are key to assessing hydrological responses and associated impacts in semi-arid mountainous basins all around the Globe.

Bias analysis on socioeconomic status and lung cancer in a pooled international case-control study

Abstract Background Low socioeconomic status (SES) groups showed increased lung cancer risks even after adjustment for smoking habits and other exposures to lung carcinogens. Although several biases were often indicated and discussed, only few studies quantified the impact of potential biases. Methods We conducted a bias analysis on the association of lung cancer and SES using data from the SYNERGY project, including 12 case-control studies with 18 study centres from Europe and Canada (16,550 cases, 20,147 controls). SES in quartiles was derived from the International Socio-Economic Index of occupational status (ISEI). Odds ratios (OR) with 95% confidence intervals (CI) were estimated by logistic regression adjusting for age, study centre, and smoking. In addition, we estimated natural direct SES effects and natural indirect smoking effects by inverse odds ratio weighting. In a multiple quantitative bias analysis, we considered impacts of misclassification of smoking status, selection bias, and unmeasured mediator-outcome confounding by a protective genetic factor, and created 95% simulation intervals (SI) by bootstrap. All analyses were stratified by sex. Results Adjustment for smoking as well as natural effects estimation showed that nearly half of lung cancer risks of lower SES groups in men and up to one third in women were attributable to smoking. Consideration of all types of bias reduced lung-cancer risks in the fully adjusted logistic regression models, with the strongest impact by selection bias: For the 4th versus 1st (highest) ISEI quartile OR decreased from 1.83 (1.69-1.98 CI) to 1.50 (1.30-1.73 SI) in men, and OR 1.48 (1.27-1.72 CI) to 1.20 (0.96-1.53 SI) in women. Conclusions Smoking is the main target for prevention of lung cancer, along with occupational and environmental exposures, in particular in lower SES groups. This finding remains, though our analysis revealed reduced lung-cancer risks of lower SES groups after multiple bias adjustment. Key messages • Impact of potential biases was quantified in the association of SES and lung cancer. • Lung cancer risks were partially attributable to smoking and multiple biases.

Socioeconomic status, smoking, and lung cancer: mediation and bias analysis in the SYNERGY study.

Increased lung-cancer risks for low socioeconomic status (SES) groups are only partially attributable to smoking habits. Little effort has been made to investigate the persistent risks related to low SES by quantification of potential biases. Based on 12 case-control studies, including 18 centers of the international SYNERGY project (16,550 cases, 20,147 controls), we estimated controlled direct effects (CDE) of SES on lung cancer via multiple logistic regression, adjusted for age, study center, and smoking habits, and stratified by sex. We conducted mediation analysis by inverse odds ratio weighting to estimate natural direct effects (NDE) and natural indirect effects via smoking habits. We considered misclassification of smoking status, selection bias, and unmeasured mediator-outcome confounding by genetic risk, both separately as well as by multiple quantitative bias analysis, using bootstrap to create 95% simulation intervals (SI). Mediation analysis of lung-cancer risks for SES estimated mean proportions of 43% in men and 33% in women attributable to smoking. Bias analyses decreased direct effects of SES on lung cancer, with selection bias showing the strongest reduction in lung-cancer risk in the multiple bias analysis. Lung-cancer risks remained increased for lower SES groups, with higher risks in men [4th versus 1st (highest) SES quartile: CDE 1.50 (SI 1.32-1.69)] than women [CDE 1.20 (SI 1.01-1.45)]. NDE were similar to CDE, particularly in men. Bias adjustment lowered direct lung-cancer risk estimates of lower SES groups. However, risks for low SES remained elevated, likely attributable to occupational hazards or other environmental exposures.

A Forensic Investigation of Climate Model Biases in Teleconnections: The Case of the Relationship Between ENSO and the Northern Stratospheric Polar Vortex

AbstractTeleconnections are crucial in shaping climate variability and regional climate change. The fidelity of teleconnections in climate models is important for reliable climate projections. As the observed sample size is limited, scientific judgment is required when models disagree with observed teleconnections. We illustrate this using the example of the relationship between El Niño‐Southern Oscillation (ENSO) and the northern stratospheric polar vortex (SPV), where the MIROC6 large ensemble exhibits an ENSO‐SPV correlation opposite in sign to observations. Yet the model well captures the upward planetary‐wave propagation pathway through which ENSO is known to affect the SPV. We show that the discrepancy arises from the model showing an additional linkage related to horizontal stratospheric wave propagation. Observations do not provide strong statistical evidence for or against the existence of this linkage. Thus, depending on the research purpose, a choice has to be made in how to use the model simulations. Under the assumption that the additional linkage is spurious, a physically‐based bias adjustment is applied to the SPV, which effectively aligns the modeled ENSO‐SPV relationship with the observations, and thereby removes the model‐observations discrepancy in the surface air temperature response. However, if one believed that the additional linkage was genuine and was undersampled in the observations, a different approach could be taken. Our study emphasizes that caution is needed when concluding that a model is not suitable for studying teleconnections. We propose a forensic approach and argue that it helps to better understand model performance and utilize climate model data more effectively.

Survival Analysis of Conversion Surgery in Borderline Resectable and Locally Advanced Unresectable Pancreatic Ductal Adenocarcinoma Addressing Selection and Immortal Time Bias: A Retrospective Single-Center Study.

The purpose of this study was to provide a detailed evaluation of the oncological advantages of surgery following neoadjuvant chemotherapy (NAC) for patients with borderline resectable (BR) or unresectable (UR) pancreatic ductal adenocarcinoma (PDAC), with a focus on minimizing biases. Recently, NAC has become the standard care for BR or UR locally advanced (UR-LA) PDAC, however, many studies have assessed survival benefits and favorable variables without consideration for biases, particularly immortal time bias. This study included patients diagnosed with BR or UR-LA PDAC at Juntendo University Hospital from 2019 to 2022. To mitigate bias, we applied methods such as propensity score matching (PSM), time-dependent covariate Cox proportional hazard regression analysis (TDC), landmark analysis, and multivariable Cox proportional hazards regression model. The study analyzed 124 patients, dividing them into a surgery group (n = 57) and a chemotherapy-only group (n = 67). After PSM, there were 21 matched pairs. Survival analysis using TDC analysis showed that the surgery group had significantly better overall survival compared with the chemotherapy-only group in both the entire cohort and the matched pairs. Cox regression analysis of the entire cohort also revealed a similar superiority of surgery, while the landmark analysis showed varying results depending on the landmark setting. After careful adjustment for selection and immortal time biases, surgery following NAC appears to significantly extend survival in patients with BR or UR PDAC.

How Does Survey Timing Influence Apparent Wasting Trends? A Case Study from Senegal

BackgroundChild wasting is known to exhibit seasonal patterns, but few studies have examined how the seasonality of wasting affects tracking of wasting trends over multiyear periods. ObjectivesWe explored the seasonality of wasting in Senegal relative to multiyear changes and examined implications for tracking. We tested whether month-fixed effects reduced bias in estimating longer-term wasting trends given variation in survey timing. MethodsThe average prevalence of child wasting (weight-for-height z-score < −2) and 95% confidence intervals were calculated by month and year from the continuous Demographic and Health Surveys (DHS) from 2012–2019. Peak and low wasting season estimates were defined as the 4 highest and the 4 lowest months of average wasting prevalence. Month-adjusted annual wasting estimates were generated using month-fixed effects linear regression, and the effectiveness of this method of bias adjustment was examined in simulated datasets. ResultsNationally, wasting fluctuated from 2013–2019, with the lowest annual prevalence of 6.0% (95% confidence interval [CI]: 5.1, 7.0%) recorded in 2014 and the highest in 2017 at 9.0% (95% CI: 8.3, 9.8%). Pooled across years, the peak wasting season prevalence was 8.8% (95% CI: 8.3, 9.3%), and low wasting season prevalence was 6.4% (95% CI: 5.7, 7.1%). Month-adjusted wasting estimates did not differ notably from raw wasting prevalence estimates. Simulations demonstrated that adjusting for months reduces bias in wasting when surveys are conducted 1 or 2 mo apart across waves but fails to reliably do so when surveys are conducted in different seasons across waves. ConclusionsSeasonal fluctuations in the prevalence of wasting can be large enough to bias the interpretation of multiyear trends. Efforts should be made to conduct national surveys at the same time of year. Seasonality adjustment using month-fixed effects works more reliably when the differences in survey periods across waves are minimal.

Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning

Hydrological models that are used to analyse flood risk induced by tropical cyclones often input ERA5 reanalysis data. However, ERA5 precipitation has large systematic biases, especially over heavy precipitation events like Tropical Cyclones, compromising its usefulness in such scenarios. Few studies to date have performed bias correction of ERA5 precipitation and none of them for extreme rainfall induced by tropical cyclones. Additionally, most existing works on bias adjustment focus on adjusting pixel-wise metrics of bias, such as the Mean Squared Error (MSE). However, it is equally important to ensure that the rainfall peaks are correctly located within the rainfall maps, especially if these maps are then used as input to hydrological models. In this paper, we describe a novel machine learning model that addresses both gaps, RA-Ucmpd, based on the popular U-Net model. The key novelty of RA-Ucmpd is its loss function, the compound loss, which optimizes both a pixel-wise bias metric (the MSE) and a spatial verification metric (a modified version of the Fractions Skill Score). Our results show how RA-Ucmpd improves ERA5 in almost all metrics by 3-28%—more than the other models we used for comparison which actually worsen the total rainfall bias of ERA5—at the cost of a slightly increased (3%) error on the magnitude of the peak. We analyse the behaviour of RA-Ucmpd by visualizing accumulated maps of four particularly wet tropical cyclones and by dividing our data according to the Saffir-Simpson scale and to whether they made landfall, and we perform an error analysis to understand under what conditions our model performs best.

Intersensor Calibration of Spaceborne Passive Microwave Radiometers and Algorithm Tuning for Long-Term Sea Ice Trend Analysis Based on AMSR-E Observations

Sea ice monitoring is key to analyzing the Earth’s climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused by differences in PMRs should be eliminated to obtain objective SIE trends. Most sea ice products have been analyzed for long-term trends with a bias adjustment based on the coarse resolution special sensor microwave imager (SSM/I) in operation for the longest period. However, since 2002, Japanese microwave radiometers of the Advanced Microwave Scanning Radiometer (AMSR) series, which have the highest spatial resolution in PMR, have been available. In this study, we developed standardization techniques for processing SIE including calibration of the brightness temperature (TB), tuning the sea ice concentration (SIC) algorithm, and adjusting the SIC threshold to retrieve a consistent SIE trend based on the AMSR for the Earth Observing System (AMSR-E, one of the AMSR that operated from May 2002 to October 2011). Analysis results showed that the root-mean-square error between AMSR-E SICs and those of moderate resolution imaging spectroradiometer (MODIS) was 15%. In this study, SIE was defined as the sum of the areas where the AMSR-E SIC was &gt;15%. When retrieving SIE, we adjusted the SIC threshold for each PMR to be consistent with the SIE calculated based on the 15% SIC threshold for AMSR-E. We then calculated a time-series of the SIE trends over approximately 45 years using the adjusted SIE data. Therefore, we revealed the dramatic decrease in global sea ice extent since 1978. This technique enables retrieval of more accurate long-term sea ice trends for more than half a century in the future.

A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard

Hydrologists and civil engineers often use design storms to assess flash flood hazards in urban, rural, and mountainous catchments. These synthetic storms are not representations of real extreme rainfall events, but rather simplified versions parameterized to mimic extreme precipitation statistics often obtained from intensity–duration–frequency (IDF) curves. To construct design storms for the future climate, it is thus necessary first to recalculate IDF curves to represent rainfall under warmer conditions. We propose a framework for adjusting IDF curves and design storms to future climate conditions using the TENAX model, a novel statistical approach that can provide future short-duration precipitation return levels based on projected temperature changes. For most applications, information from climate models at the daily scale can be used to construct design storms at the sub-hourly scale without any downscaling or bias adjustment. Our approach is illustrated through a re-parameterization of the Chicago Design Storm (CDS) in the context of climate change. As a case study demonstration, we apply the TENAX model to data from the city of Zurich to calculate changes in the historical IDF curve for durations ranging from 10 min to 3 h. We then construct synthetic 100-year return period design storms based on the CDS for present and future climates and use the CAFlood model to produce flood inundation maps to assess changes in flood hazard. The codes for adapting design storms to climate change are simple to implement, easily applicable by practitioners, and made freely available.

Change of global land extreme temperature in the future

Understanding future temperature extremes is pivotal to preparing for and mitigating the impacts of climate change. This study proposed machine learning techniques to develop a multi-model ensemble model for high-resolution projection of global land temperature extremes under different emission scenarios, hence providing enhanced precision over previous climate model projections. By utilizing the NEX-GDDP-CMIP6 dataset with bias adjustment and the Gradient Booster algorithm, we reduced the biases that existed in Global Climate Models. The model significantly reduces the root mean square errors (RMSEs) for both the daily maximum and daily minimum temperature extremes. A future scenario analysis revealed that global temperature extremes would substantially increase under high-emission scenarios, highlighting the urgency for stringent emission reduction commitments. This study also identified regions like Greenland, the Tibetan Plateau, and the regional Arctic Archipelago as potential hotspots of temperature extremes under these scenarios. The multi-model ensemble approach, tuned with machine learning and driven by high-resolution data, contributes to climate science by providing refined insights into future temperature extremes, thereby offering direction to climate change mitigation and adaptation strategies.

The factors associated with the provision of public access defibrillation in Japan − A nationwide cohort study

AimThe association between out-of-hospital cardiac arrest (OHCA) and the appropriate provision of public access defibrillation (PAD) remains unclear. This study aimed to evaluate the factors associated with whether or not PAD was provided. MethodsThis retrospective cohort study utilized the All-Japan Utstein and Emergency Transport Registries in 2021. We included OHCA patients who were applied to automated external defibrillators (AEDs) by bystanders and were deemed eligible for defibrillation by an AED. We defined PAD provided or no PAD provided based on bystander defibrillation. Multivariable logistic regression analysis with the Firth bias adjustment method was employed to estimate the adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for the exploratory evaluation of factors associated with PAD provided. Results1949 patients were eligible for analysis (PAD provided, n = 1696 [87.0%]; no PAD provided, n = 253 [13.0%]). Factors positively associated with PAD provided were male (AOR [95% CI], 1.61 [1.17–2.21]; vs. female), other public place incidence (AOR [95% CI], 10.65 [1.40–1367.54]; vs. public place), non-family member witnessed (AOR [95% CI], 2.51 [1.86–3.42]; vs. unwitnessed) and conventional cardiopulmonary resuscitation (CPR), (AOR [95% CI], 1.75 [1.17–2.67]; vs. hands-only CPR). Conversely, factors negatively associated with no PAD provided were over 65 years old (AOR [95% CI], 0.48 [0.28–0.80]; vs. 19–64 yr), night-time onset (AOR [95% CI], 0.61 [0.45–0.83]; vs. daytime), non-cardiogenic (AOR [95% CI], 0.43 [0.31–0.61]; vs. cardiogenic), home setting (AOR [95% CI], 0.33 [0.14–0.83]; vs. public place), healthcare facility setting (AOR [95% CI], 0.40 [0.23–0.66]; vs. public place), no bystander CPR (AOR [95% CI], 0.31 [0.14–0.71]; vs. hands-only CPR), and dispatcher-assistance (AOR [95% CI], 0.72 [0.53–0.97]; vs. no dispatcher-assistance). ConclusionMale patients, other public place onset, witnessed by non-family and conventional CPR were associated with PAD provide. Therefore, training skilled first responders to use AEDs appropriately is necessary.

A Dynamically Consistent ENsemble of Temperature at the Earth surface since 1850 from the DCENT dataset

Accurate historical records of Earth’s surface temperatures are central to climate research and policy development. Widely-used estimates based on instrumental measurements from land and sea are, however, not fully consistent at either global or regional scales. To address these challenges, we develop the Dynamically Consistent ENsemble of Temperature (DCENT), a 200-member ensemble of monthly surface temperature anomalies relative to the 1982–2014 climatology. Each DCENT member starts from 1850 and has a 5° × 5° resolution. DCENT leverages several updated or recently-developed approaches of data homogenization and bias adjustments: an optimized pairwise homogenization algorithm for identifying breakpoints in land surface air temperature records, a physics-informed inter-comparison method to adjust systematic offsets in sea-surface temperatures recorded by ships, and a coupled energy balance model to homogenize continental and marine records. Each approach was published individually, and this paper describes a combined approach and its application in developing a gridded analysis. A notable difference of DCENT relative to existing temperature estimates is a cooler baseline for 1850–1900 that implies greater historical warming.

Reduced mortality with antimicrobial stewardship guided by BioFire FilmArray Blood Culture Identification 2 panel in critically ill patients with bloodstream infection: A retrospective propensity score-matched study

ObjectivesTo investigate whether using the BioFire® FilmArray® Blood Culture Identification 2 panel (BCID2) leads to timely antimicrobial therapy and improves patient outcomes in critically ill patients with bloodstream infections (BSIs). MethodsThis retrospective observational study included patients with BSIs admitted to the intensive care unit from July 1, 2021, to August 31, 2023. Patients were divided into groups receiving appropriate or inappropriate antimicrobial therapy. Those receiving inappropriate therapy underwent adjustments using standard-of-care (SOC) testing or BCID2. Propensity score matching (PSM) was performed on the original cohort (Model 1) and a time-window bias-adjusted cohort (Model 2). Clinical impact of BCID2-guided antimicrobial adjustment was analysed in both models. ResultsA total of 181 patients received inappropriate antimicrobial therapy, with 33 undergoing BCID2 testing and 148 undergoing SOC testing. Following PSM and time-window bias adjustment, 66 patients were analysed in Model 1 and 46 patients in Model 2. BCID2 significantly reduced the median time to appropriate antimicrobial therapy (40.8 vs. 74.0 h in Model 1; 42.8 vs. 68.9 h in Model 2) and the day-28 mortality rate (27.8% vs. 77.1%, P < 0.001 in Model 1; 23.5% vs. 58.6%, P = 0.021 in Model 2). In multivariate regression analysis, BCID2-guided antimicrobial adjustment was an independent prognostic factor for day-28 mortality (adjusted odds ratio [aOR] 0.07 in Model 1 and aOR 0.12 in Model 2). ConclusionBCID2-guided antimicrobial stewardship was associated with a shorter time to appropriate antimicrobial therapy and reduced day-28 mortality in critically ill patients with BSIs receiving inappropriate antimicrobial therapy.

Anaesthesia modality on endovascular therapy outcomes in patients with large infarcts: a post hoc analysis of the ANGEL-ASPECT trial

ObjectivesEndovascular therapy (EVT) now penetrates the once obscure realm of large infarct core volume acute ischaemic stroke (LICV-AIS). This research aimed to investigate the potential correlation between different anaesthetic approaches...

Frequency Bias Causes Overestimation of Climate Change Impacts on Global Flood Occurrence

AbstractThe frequency change of 100‐year flood events is often determined by fitting extreme value distributions to annual maximum discharge from a historical base period. This study demonstrates that this approach may significantly bias the computed flood frequency change. An idealized experiment shows frequency bias exceeding 100% for a 50‐year base period. Further analyses using Monte Carlo simulations, mathematical derivations, and hydrological model outputs reveal that bias magnitude inversely relates to base period length and is weakly influenced by the generalized extreme value distribution's shape parameter. The bias, persisting across different estimation methods, implies floods may exceed local defenses designed based on short historical records more often than expected, even without climate change. We introduce a frequency bias adjustment method, which significantly reduces the projected rise in global flood occurrence. This suggests a substantial part of the earlier projected increase in flood occurrence and impacts is not attributable to climate change.

Elevation-dependent biases of raw and bias-adjusted EURO-CORDEX regional climate models in the European Alps

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate.