Calibration Measurements Research Articles

Assessing the Accuracy of Cardiovascular Disease Prediction Using Female-Specific Risk Factors in Women Aged 45 to 69 Years in the UK Biobank Study.

Cardiovascular disease (CVD) is the leading cause of mortality in women. We aimed to assess whether adding female-specific risk factors to traditional factors could improve CVD risk prediction. We used a cohort of women from the UK Biobank Study aged 45 to 69 years, free of CVD at baseline (2006-2010) followed until the end of 2019. We developed Cox proportional hazards models using the risk factors included in 3 contemporary CVD risk calculators: Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT. We added each of the following female-specific risk factors, individually and all together, to determine if these improved measures of discrimination and calibration for predicting CVD: early menarche (<11 years), endometriosis, excessive, frequent or irregular menstruation, miscarriage, number of miscarriages, number of stillbirths, infertility, preeclampsia or eclampsia, gestational diabetes (without subsequent type 2 diabetes), premature menopause (<40 years), early menopause (<45 years), and natural or surgical early menopause (menopause <45 years or timing of menopause reported as unknown and oophorectomy reported at age <45). In the model of 135 142 women (mean age, 57.5 years; SD, 6.8) using risk factors from Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, CVD incidence was 5.3 per 1000 person-years. The c-indices for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models were 0.710, 0.713, and 0.718, respectively. Adding each of the female-specific risk factors did not improve the c-index, the net reclassification index, the integrated discrimination index, the slope of the regression line for predicted versus observed events, and the Brier score or plots of calibration. Adding all female-specific risk factors simultaneously increased the c-index for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models to 0.712, 0.715, and 0.720, respectively. Although several female-specific factors have been shown to be early indicators of CVD risk, these factors should not be used to reclassify risk in women aged 45 to 69 years when considering whether to commence a blood pressure or lipid-lowering medication.

Measurement of the electric potential and the magnetic field in the shifted analysing plane of the KATRIN experiment

The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after 5 years of data acquisition. The sensitivity is affected by the increased rate of the background electrons from KATRIN’s main spectrometer. A special shifted-analysing-plane (SAP) configuration was developed to reduce this background by a factor of two. The complex layout of electromagnetic fields in the SAP configuration requires a robust method of estimating these fields. We present in this paper a dedicated calibration measurement of the fields using conversion electrons of gaseous 83mKr, which enables the neutrino-mass measurements in the SAP configuration.

Clinical predictive models for recurrence and survival in treated laryngeal and hypopharyngeal cancer: a systematic review and meta-analysis

BackgroundThe limitations of the traditional TNM system have spurred interest in multivariable models for personalized prognostication in laryngeal and hypopharyngeal cancers (LSCC/HPSCC). However, the performance of these models depends on the quality of data and modelling methodology, affecting their potential for clinical adoption. This systematic review and meta-analysis (SR-MA) evaluated clinical predictive models (CPMs) for recurrence and survival in treated LSCC/HPSCC. We assessed models’ characteristics and methodologies, as well as performance, risk of bias (RoB), and applicability.MethodsLiterature searches were conducted in MEDLINE (OVID), Embase (OVID) and IEEE databases from January 2005 to November 2023. The search algorithm used comprehensive text word and index term combinations without language or publication type restrictions. Independent reviewers screened titles and abstracts using a predefined Population, Index, Comparator, Outcomes, Timing and Setting (PICOTS) framework. We included externally validated (EV) multivariable models, with at least one clinical predictor, that provided recurrence or survival predictions. The SR-MA followed PRISMA reporting guidelines, and PROBAST framework for RoB assessment. Model discrimination was assessed using C-index/AUC, and was presented for all models using forest plots. MA was only performed for models that were externally validated in two or more cohorts, using random-effects model. The main outcomes were model discrimination and calibration measures for survival (OS) and/or local recurrence (LR) prediction. All measures and assessments were preplanned prior to data collection.ResultsThe SR-MA identified 11 models, reported in 16 studies. Seven models for OS showed good discrimination on development, with only one excelling (C-index &amp;gt;0.9), and three had weak or poor discrimination. Inclusion of a radiomics score as a model parameter achieved relatively better performance. Most models had poor generalisability, demonstrated by worse discrimination performance on EV, but they still outperformed the TNM system. Only two models met the criteria for MA, with pooled EV AUCs 0.73 (95% CI 0.71-0.76) and 0.67 (95% CI 0.6-0.74). RoB was high for all models, particularly in the analysis domain.ConclusionsThis review highlighted the shortcomings of currently available models, while emphasizing the need for rigorous independent evaluations. Despite the proliferation of models, most exhibited methodological limitations and bias. Currently, no models can confidently be recommended for routine clinical use.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021248762, identifier CRD42021248762.

A 3D calibration method for binocular vision system

Abstract Binocular vision system calibration is extremely important in three-dimensional (3D) measurement. During the calibration process, the establishment of an appropriate objective function has a direct impact on the calibration accuracy. For common two-dimensional (2D) calibration methods, differences in 2D calibration and 3D measurement coordinate systems result in loss of accuracy. Furthermore, for the newly proposed 3D calibration methods, the epipolar constraints in the 2D pixel coordinate system and the reconstruction errors in the 3D physical space need to be balanced by weights, resulting in uncertainties in the calibration results. To address these issues, we propose a novel calibration method that minimizes the distances between the control point and the left and right camera rays. The distances between the control points and the camera rays describe the 3D reconstruction errors, and the distances between the left camera and the right camera rays describe the geometry information between the two cameras to ensure that the epipolar constraints are satisfied. As a result, these two types of constraints/errors are measured under the same 3D coordinate system. Therefore, the epipolar constraints and the reconstruction errors are naturally balanced. We have conducted computer simulations and real experiments, and the results show that the proposed calibration method is effective in improving the accuracy compared with the state-of-the-art methods.

100% humidity independent PTR-MS: Novel method and proof-of-concept**Dedicated to Tilmann D. Märk on the occasion of his 80th birthday.

Abstract It has been well-known and studied that the quantification of various compounds with Proton-Transfer-Reaction – Mass Spectrometry (PTR-MS) is dependent on the sample gas humidity. For example, if the formaldehyde concentration in outdoor air remains constant, but the weather and thus the humidity changes, the measured signal intensity for protonated formaldehyde will change. Established countermeasures against this effect include labor-intensive calibrations at different humidity levels and subsequent data correction, the use of filters or membranes to dry the sample air, which will also trap analytes of interest, and the introduction of excessive amounts of humidity into the PTR reaction region to reduce the relative but not the absolute humidity changes caused by the sample gas, which alters the well-defined ion chemistry and again limits quantification accuracy. &amp;#xD;Here, we present a novel solution to the humidity dependence problem that comes virtually without any drawbacks, does not require any additional labor by the user, and does not diminish analytes of condensable nature. A target humidity for the PTR reaction region is chosen before the actual measurement. Via the controlled injection of water vapor into the reaction region, the humidity is kept constant throughout the analysis, independent of the sample gas constitution. Therefore, it can be excluded that any changes in the acquired ion signal intensities are artifacts caused by changing sample humidity, and, if required, calibration measurements only have to be performed at one humidity level. &amp;#xD;We introduce the instrumental adaption necessary for using the novel method and subsequently present proof-of-concept results of measurements in a laboratory, as well as in a "real-life" environment, utilizing two different PTR-MS instruments. &amp;#xD;

Facilitating Trust Calibration in Artificial Intelligence-Driven Diagnostic Decision Support Systems for Determining Physicians' Diagnostic Accuracy: Quasi-Experimental Study.

Diagnostic errors are significant problems in medical care. Despite the usefulness of artificial intelligence (AI)-based diagnostic decision support systems, the overreliance of physicians on AI-generated diagnoses may lead to diagnostic errors. We investigated the safe use of AI-based diagnostic decision support systems with trust calibration by adjusting trust levels to match the actual reliability of AI. A quasi-experimental study was conducted at Dokkyo Medical University, Japan, with physicians allocated (1:1) to the intervention and control groups. A total of 20 clinical cases were created based on the medical histories recorded by an AI-driven automated medical history-taking system from actual patients who visited a community-based hospital in Japan. The participants reviewed the medical histories of 20 clinical cases generated by an AI-driven automated medical history-taking system with an AI-generated list of 10 differential diagnoses and provided 1 to 3 possible diagnoses. Physicians were asked whether the final diagnosis was in the AI-generated list of 10 differential diagnoses in the intervention group, which served as the trust calibration. We analyzed the diagnostic accuracy of physicians and the correctness of the trust calibration in the intervention group. We also investigated the relationship between the accuracy of the trust calibration and the diagnostic accuracy of physicians, and the physicians' confidence level regarding the use of AI. Among the 20 physicians assigned to the intervention (n=10) and control (n=10) groups, the mean age was 30.9 (SD 3.9) years and 31.7 (SD 4.2) years, the proportion of men was 80% and 60%, and the mean postgraduate year was 5.8 (SD 2.9) and 7.2 (SD 4.6), respectively, with no significant differences. The physicians' diagnostic accuracy was 41.5% in the intervention group and 46% in the control group, with no significant difference (95% CI -0.75 to 2.55; P=.27). The overall accuracy of the trust calibration was only 61.5%, and despite correct calibration, the diagnostic accuracy was 54.5%. In the multivariate logistic regression model, the accuracy of the trust calibration was a significant contributor to the diagnostic accuracy of physicians (adjusted odds ratio 5.90, 95% CI 2.93-12.46; P<.001). The mean confidence level for AI was 72.5% in the intervention group and 45% in the control group, with no significant difference. Trust calibration did not significantly improve physicians' diagnostic accuracy when considering the differential diagnoses generated by reading medical histories and the possible differential diagnosis lists of an AI-driven automated medical history-taking system. As this was a formative study, the small sample size and suboptimal trust calibration methods may have contributed to the lack of significant differences. This study highlights the need for a larger sample size and the implementation of supportive measures of trust calibration.

Improving relative humidity measurements on Mars: new laboratory calibration measurements

Abstract. In this paper we present new calibration measurements that have been performed with the ground reference models of the relative humidity instruments of the Mars Science Laboratory (MSL), Mars 2020 and ExoMars missions. All instruments are based on capacitive sensor head technology, and they are developed, manufactured and tested by the Finnish Meteorological Institute (FMI). Calibration of capacitive humidity sensors for the Martian environment has been a challenging task and special facilities are needed in order to create Martian conditions including all relevant environmental parameters that can be accurately controlled and measured: low pressure, low temperature, carbon dioxide environment and especially humidity. A measurement campaign was performed at the German Aerospace Center (DLR) PASLAB (Planetary Analog Simulation Laboratory) to determine relative humidity calibration datasets for REMS-H, MEDA HS and METEO-H instruments in temperatures from −30 °C down to −70 °C in low-pressure CO2. In addition to the stable point humidity calibration measurements in CO2, the instrument performance was tested with the actual Martian atmosphere composition and during long continuous measurements. The new calibration dataset has already been used in the flight calibration of the MEDA HS instrument, resulting in successful calibration and excellent accuracy. The results from this campaign will further improve relative humidity measurements on Mars by providing the means to reanalyze the current calibration of the REMS-H flight model and by allowing more accurate comparison between the two instruments currently on the Martian surface.

Comparing predictive validity of Youth Level of Service/Case Management Inventory scores in Indigenous and non-Indigenous Canadian youth.

There is an increasing recognition of the necessity to establish the predictive validity of risk assessment scores within specific population subgroups, particularly those (including Indigenous peoples) who are overrepresented in the criminal justice system. I compared measures of discrimination and calibration of the Youth Level of Service/Case Management Inventory (YLS/CMI) in Indigenous and non-Indigenous youth probationers in Ontario, Canada. Compared with non-Indigenous youth, Indigenous youth would have higher risk scores and reoffense rates. The YLS/CMI would predict reoffending and time to reoffense significantly and comparably for Indigenous and non-Indigenous youth, but there would be group difference discrimination (sensitivity, specificity) and calibration (positive predictive value, negative predictive value). Justice ministry-supplied data on 400 Indigenous and non-Indigenous youth (330 male, 70 female) individually matched on key background variables were analyzed to provide measures of discrimination and calibration of the YLS/CMI, with 3-year recidivism as the primary outcome. Indigenous youth were assessed at significantly higher risk than non-Indigenous youth (d = .60); 70% of Indigenous youth and 46% of non-Indigenous youth reoffended (ϕ = .24). Overall measures of discrimination (area under the curve) and calibration (logistic regression) were significant and did not differ across groups. Cross-area under the curve results indicated that the YLS/CMI discriminated Indigenous recidivists from non-Indigenous nonrecidivists but differentiated Indigenous nonrecidivists from non-Indigenous recidivists at chance level. In addition, recidivism was underestimated for low-risk Indigenous youth compared with non-Indigenous youth, but specificity was also low; only 28% of Indigenous youth who did not reoffend were assessed as low risk. Results were largely consistent across male and female youth. Examining subgroup predictive validity using multiple indices provides important information that should inform policy and practice discussions regarding fair use of risk assessment tools. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Predicting post-COVID-19 condition in children and young people up to 24 months after a positive SARS-CoV-2 PCR-test: the CLoCk study

BackgroundPredicting which children and young people (CYP) are at the highest risk of developing post-COVID-19 condition (PCC) could improve care pathways. We aim to develop and validate prediction models for persistent PCC up to 24 months post-infection in CYP.MethodsCYP who were PCR-positive between September 2020 and March 2021, with follow-up data up to 24-months post-infection, were analysed. Persistent PCC was defined in two ways, as PCC at (a) 3, 6, 12 and 24 months post-infection (N = 943) or (b) 6, 12 and 24 months post-infection (N = 2373). Prediction models were developed using logistic regression; performance was assessed using calibration and discrimination measures; internal validation was performed via bootstrapping; the final model was adjusted for overfitting.ResultsWhile 24.7% (233/943) of CYP met the PCC definition 3 months post-infection, only 7.2% (68/943) continued to meet the PCC definition at all three subsequent timepoints, i.e. at 6, 12 and 24 months. The final models predicting risk of persistent PCC (at 3, 6, 12 and 24 months and at 6, 12 and 24 months) contained sex (female), history of asthma, allergy problems, learning difficulties at school and family history of ongoing COVID-19 problems, with additional variables (e.g. older age at infection and region of residence) in the model predicting PCC at 6, 12 and 24 months. Internal validation showed minimal overfitting of models with good calibration and discrimination measures (optimism-adjusted calibration slope: 1.064–1.142; C-statistic: 0.724–0.755).ConclusionsTo our knowledge, these are the only prediction models estimating the risk of CYP persistently meeting the PCC definition up to 24 months post-infection. The models could be used to triage CYP after infection. CYP with factors predicting longer-term symptomology, may benefit from earlier support.

3D in-system calibration method for PET detectors.

Light-sharing detector designs for positron emission tomography (PET) systems have sparked interest in the scientific community. Particularly, (semi-)monoliths show generally good performance characteristics regarding 2D positioning, energy-, and timing resolution, as well as readout area. This is combined with intrinsic depth-of-interaction (DOI) capability to ensure a homogeneous spatial resolution across the entire field of view (FoV). However, complex positioning calibration processes limit their use in PET systems, especially in large-scale clinical systems. This work proposes a new 3D positioning in-system calibration method for fast and convenient (re-)calibration and quality control of assembled PET scanners. The method targets all kinds of PET detectors that achieve the best performance with individual calibration, including complex segmented detector designs. The in-system calibration method is evaluated and empirically compared to a state-of-the-art fan-beam calibration for a small-diameter proof of concept (PoC) scanner. A simulation study evaluates the method's applicability to different scanner geometries. A PoC scanner geometry of 120mm inner diameter and 150mm axial extent was set up consisting of five identical finely segmented slab detectors (one detector under test and four collimation detectors). A 2 2Na point source was moved in a circular path inside the FoV. Utilizing virtual collimation and by selecting gamma rays incident approximately perpendicular to the detector normal of the detector under test, training data was created for the training of a 2D positioning model with the machine-learning technique gradient tree boosting (GTB). Data with oblique ray angles was acquired in the same measurement for subsequent angular DOI calibration. For this, a 2D position estimate in the detector under test was calculated first. On this basis, the DOI label was calculated geometrically from the ray path within the detector to finally establish up to 3D training data. With a mean absolute error (MAE) of 0.8 and 1.19mm full-width at half maximum (FWHM) along the planar-monolithic slab dimension, the in-system methods performed similarly within 1% to the fan-beam collimator results. The DOI performance was at ∼90% with 1.13mm MAE and 2.47mm FWHM to the fan-beam collimator. Analytical calculations suggest an improved performance for larger scanner geometries. The functionality of the 3D in-system positioning calibration method was successfully demonstrated with the measurements within a PoC scanner configuration with similar positioning performance as the bench-top fan-beam setup. The in-system calibration method can be used to calibrate and test fully assembled PET systems to enable more complex light-sharing detector architectures in, for example, large PET systems with many detectors. The acquired data can further be used for more complex energy and time calibrations.

Development of a Large-Aperture Coordinate Precision Measurement Instrument Using Differential Geometric Error Weighting

The accuracy of traditional measuring machines is affected by the measuring range and sensitive geometric errors, and it is not possible to combine large caliber and high-precision measurements. This study proposes a differential geometric error-weighting method for designing a high-precision, large-diameter measuring machine. The machine utilized a zero-Abbe arm structure and applied the rigid body theory and small-angle hypothesis to model geometric errors. Weights were calculated for 23 geometric errors, identifying eight sensitive ones. A picometer-precision laser interferometer (quDIS) with a theoretical positioning accuracy of 0.2 nm/mm and standard flat rulers are used to ensure highly accurate positioning of the Y-axis/Z-axis of the measuring platform and reduce the straightness of both axes by approximately 75%, with radial and axial runout of the rotary table under 100 nm. The development and design method of the high-precision measuring machine proposed in this study is applicable to large-diameter high-precision flexible measurement, and the accurate control of measuring machine movement accuracy is realized by calculating the geometric error weights.

Measurement Technique for the Absolute Acoustic Nonlinearity Parameter Employing Only a Narrowband Pulser

In the nonlinear ultrasonic technique, the measurement of the absolute acoustic nonlinearity parameter is employed to obtain quantitative material nonlinearity. In the piezoelectric method, this measurement involves two processes: calibration and harmonic measurements. In the calibration process, a transfer function is obtained, and distortion of the propagating wave is measured in the harmonic measurement. In the conventional method for this measurement, a narrowband pulser is typically used for the harmonic measurement, while a broadband pulser is used for the calibration measurement. In this study, we propose a technique that utilizes a narrowband pulser for both harmonic and calibration measurements. First, we introduced the proposed method to measure the transfer function in the calibration measurement employing the narrowband pulser. Then, we compared the results of the transfer functions and the absolute acoustic nonlinearity parameters obtained using both a narrowband and broadband pulser in the calibration. Based on the experimental results, we found that the results obtained using both the proposed and conventional methods are highly similar, confirming the validity of the proposed measurement technique. In conclusion, even though we exclusively employ the narrowband pulser, we can accurately measure the absolute nonlinearity parameter, leading to a reduction in the required measurement equipment.

A Novel Method to Predict Carbohydrate and Energy Expenditure During Endurance Exercise Using Measures of Training Load.

Sports nutrition guidelines recommend carbohydrate (CHO) intake be individualized to the athlete and modulated according to changes in training load. However, there are limited methods to assess CHO utilization during training sessions. We aimed to (1) quantify bivariate relationships between both CHO and overall energy expenditure (EE) during exercise and commonly used, non-invasive measures of training load across sessions of varying duration and intensity and (2) build and evaluate prediction models to estimate CHO utilization and EE with the same training load measures and easily quantified individual factors. This study was undertaken in two parts: a primary study, where participants performed four different laboratory-based cycle training sessions, and a validation study where different participants performed a single laboratory-based training session using one of three exercise modalities (cycling, running, or kayaking). The primary study included 15 cyclists (five female; maximal oxygen consumption [ O2max], 51.9 ± 7.2mL/kg/min), the validation study included 21 cyclists (seven female; O2max 53.5 ± 11.0mL/kg/min), 20 runners (six female; O2max 57.5 ± 7.2mL/kg/min), and 18 kayakers (five female; O2max 45.6 ± 4.8mL/kg/min). Training sessions were quantified using six training load metrics: two using heart rate, three using power, and one using perceived exertion. Carbohydrate use and EE were determined separately for aerobic (gas exchange) and anaerobic (net lactate accumulation, body mass, and O2 lactate equivalent method) energy systems and summed. Repeated-measures correlations were used to examine relationships between training load and both CHO utilization and EE. General estimating equations were used to model CHO utilization and EE, using training load alongside measures of fitness and sex. Models were built in the primary study and tested in the validation study. Model performance is reported as the coefficient of determination (R2) and mean absolute error, with measures of calibration used for model evaluation and for sport-specific model re-calibration. Very-large to near-perfect within-subject correlations (r = 0.76-0.96) were evident between all training load metrics and both CHO utilization and EE. In the primary study, all models explained a large amount of variance (R2 = 0.77-0.96) and displayed good accuracy (mean absolute error; CHO = 16-21g [10-14%], EE = 53-82kcal [7-11%]). In the validation study, the mean absolute error ranged from 16-50g [15-45%] for CHO models to 53-182kcal [9-31%] for EE models. The calibrated mean absolute error ranged from 9-20g [8-18%] for CHO models to 36-72kcal [6-12%] for EE models. At the individual level, there are strong linear relationships between all measures of training load and both CHO utilization and EE during cycling. When combined with other measures of fitness, EE and CHO utilization during cycling can be estimated accurately. These models can be applied in running and kayaking when used with a calibration adjustment.

Prediction models for outcomes in people with low back pain receiving conservative treatment: a systematic review

To identify, critically appraise and evaluate the performance measures of the available prediction models for outcomes in people with low back pain (LBP) receiving conservative treatment. In this systematic review, literature searches were conducted in Embase, Medline, CINAHL from their inception until February/2024. Studies containing follow-up assessment (e.g., prospective cohort studies, registry-based studies) investigating prediction models of outcomes (e.g., pain intensity and disability) for people with LBP receiving conservative treatment were included. Two independent reviewers performed the study selection, the data extraction using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and risk of bias assessment using the Prediction model Risk of Bias Assessment (PROBAST). Findings of individual studies were reported narratively taking into account the discrimination and calibration measures of the prediction models. Seventy-five studies developing or investigating the validity of 216 models were included in this review. Most prediction models investigated people receiving physiotherapy treatment and most models included socio-demographic variables, clinical features, and self-reported measures as predictors. The discriminatory capacity of the internal validity of the 27 prediction models for pain intensity varied greatly showing a c-statistic ranging from 0.48 to 0.94. Similarly, the discriminatory capacity for 31 models for disability had the same pattern showing a c-statistic ranging from 0.48 to 0.86. The calibration measures of the internal validity of the prediction models predicting pain intensity and disability showed to be adequate. Only one of three studies testing the external validity of models to predict pain intensity and disability and reported both discrimination and calibration measures, which showed to be inadequate. The prediction models predicting the secondary outcomes (e.g., self-reported recovery, quality of life, return to work) showed varied performance measures for internal validity, and only two studies tested the external validity of models although they did not provide performance the performance measures. Several prediction models have been developed for people with LBP receiving conservative treatment, however most show inadequate discriminatory validity. A few studies externally validated the prediction models and future studies should focus on testing this before implementing in clinical practice.

Multi-Institutional Model to Predict Intensive Care Unit Length of Stay after Cardiac Surgery

Intensive care unit length of stay (ICU LOS) accounts for a large percent of inpatient cost following cardiac surgery. The Society of Thoracic Surgeons (STS) risk calculator predicts total LOS but does not discriminate between ICU and non-ICU time. We sought to develop a predictive model of prolonged ICU LOS. Adult patients undergoing STS index operations within a regional collaborative (2014-2021) were included. Prolonged ICU LOS was defined as ICU care for ≥72 hours postoperatively. A logistic regression model was utilized to develop a prediction model for the prolonged ICU LOS with pre-specified risk factors identified from our previous single center study. Internal prediction model validation was determined by bootstrapping resampling method. The prediction model performance was assessed by measures of discrimination and calibration. We identified 37,519 patients that met inclusion criteria with 11,801 (31.5%) patients experiencing prolonged ICU stay. From the logistic regression model, there were significant associations between prolonged ICU LOS and all pre-specified factors except sleep apnea (all p<0.05). MELD, preoperative intra-aortic balloon pump use, and procedure types were the most significant predictors of prolonged ICU LOS (all p<0.0001). Our prediction model had not only a good discrimination power (bootstrapped-corrected C-index=0.71) but also excellent calibration (bootstrapped-corrected mean absolute error=0.005). Prolonged ICU stay following cardiac surgery can be predicted with good predictive accuracy utilizing preoperative data and may aid in patient counseling and resource allocation. Through use of a state-wide database, the application of this model may extend to other practices.

A calibration method of line laser 3D gear measurement system

It is of great importance to ensure the accuracy of measurements taken by the line laser three-dimensional (3D) gear measurement system through the calibration of system in an accurate manner. This paper established a relevant measurement model based on the principle of line laser 3D gear measurement using generalized polar coordinates, designed a dedicated Polyhedral Artefact (PA), and proposed an accurate calibration method to address the challenges posed by the representation of 6 degrees of freedom (DOF) parameters for line laser sensor data in measurement space. Among them, the PA includes features such as cylindrical plane, planar plane, and V-groove etc., which can meet the requirements of high-precision machining while maintaining consistency with gear installation accuracy to further improve the accuracy of sensor pose parameters. The calibration method uses nonlinear optimization equations with point cloud data to simultaneously solve the required parameters for accurate measurement. Calibration and gear measurement experiments reveal that the standard deviation and range deviation of the total tooth profile deviation, obtained from 10 repeated measurements of a single tooth flank, are 0.244 μm and 0.86 μm, respectively. The results of the tooth profile measurements after the calibration of the system show good consistency compared with the contact measurements, which verifies the effectiveness of the method.

Black Body Cavity Apparatus for Measuring the Emissivity of Nickel-Titanium-Based Shape-Memory Alloys and Other Metals

Nickel-titanium (NiTi)-based shape-memory alloys (SMAs) have various applications in biomedicine, actuators, aerospace technologies, and elastocaloric devices, due to their shape-memory and super-elastic effects. These effects are induced in SMAs by a reversible martensitic phase transformation. This transformation can be achieved by applying stress or temperature differences. It is extremely difficult to measure the temperature of NiTi elements with contact thermometers because they disturb the phase transformation phenomenon. An alternative approach is contactless infrared thermography for which accurate emissivity data are mandatory. To determine the temperature distribution in NiTi components with an infrared camera, a newly constructed apparatus is presented to measure the emissivity of metals. For this purpose, a state-of-the-art infrared camera was employed to analyze the emissivity behavior with temperature, especially during the phase transformation. The emissivity of the NiTi samples was systematically studied by comparing them with a reference-quality black body cavity (BBC) having an emissivity better than 0.995. Calibration measurements revealed that the maximum deviation of the BBC temperature measured with the infrared camera was only 0.15% (0.45 K) from its temperature measured with the built-in contact thermometers. The apparatus was validated by measuring the emissivity of a polished aluminum sample and found to be in good agreement with literature data, particularly for temperatures above 340 K. Finally, the emissivity of two rough and one polished NiTi samples was measured covering the temperature range from 313 K to 423 K with an expanded uncertainty of about 0.02 (k=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k=2$$\\end{document}). The studied NiTi samples have an atomic percent composition of Ni42.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {Ni}_{42.5}$$\\end{document}Ti49.9\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {Ti}_{49.9}$$\\end{document}Cu7.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {Cu}_{7.5}$$\\end{document}Cr0.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {Cr}_{0.1}$$\\end{document}. We observed that the emissivity of NiTi varies between 0.17 and 0.31 depending on temperature. As the temperature rises, the emissivity increases rapidly during the phase transformation, and it decreases gradually beyond the austenite finish temperature. In addition, the emissivity of NiTi depends on the microstructure of the martensite and austenite phases and the surface roughness of the samples.

DETERMINATION OF CALIBRATION MEASUREMENT UNCERTAINTY EFFECT FOR DIFFERENT AMBIENT TEMPERATURES UPON DIGITAL THERMOMETER READOUTS

Calibration of digital thermometer is usually performed in laboratory environmental conditions, mostly at 23 degree Celsius Plus Minus 3 degree Celsius. However, after calibration, there may be circumstances where the device is used at different temperatures. Generally for on-site processes, it is not much possible to provide laboratory environmental conditions. In that situation, it is necessary to determine how the device responds to different temperatures. In this study, calibration measurement uncertainty effect for different ambient temperatures upon digital thermometer readout is investigated by exposing digital thermometer readout to temperature values of 10 degree Celsius and 40 degree Celsius.

External validation and calibration of the HoLISTIC Consortium's advanced-stage Hodgkin lymphoma international prognostic index (A-HIPI) in the Brazilian Hodgkin lymphoma registry.

The Hodgkin lymphoma International Study for Individual Care (HoLISTIC) Consortium's A-HIPI model, developed in 2022 for advanced-stage classical Hodgkin lymphoma (cHL), predicts survival within 5 years amongst newly diagnosed patients. This study validates its performance in the Brazilian Hodgkin lymphoma registry. By 2022, the Brazilian HL registry included 1357 cHL patients, with a median 5-year follow-up. Probabilities for 5-year progression-free survival (PFS) and overall survival (OS) were calculated using A-HIPI-model equations. Discrimination (Harrell C-statistic/Uno C-statistic) and calibration measures assessed external validation and calibration. Lab values beyond the allowed range were excluded, mirroring the initial A-HIPI analysis. A total of 694 advanced-stage cHL patients met the original inclusion criteria (age 18-65 years, Stage IIB-IV). Median age was 31 years; 46.3% were females. Stage distribution was IIB (33.1%), III (27.4%), IV (39.5%). Bulky disease in 32.6%. Five-year PFS and OS were 68.4% and 86.0%, respectively. Harrell C-statistics were 0.60 for PFS and 0.69 for OS, and Uno C-statistics were 0.63 for PFS and 0.72 for OS. Calibration plots demonstrated well-calibrated predictions with calibration slopes of 0.91 and 1.03 for 5-year OS and PFS, respectively. Despite differing patient, clinical characteristics, and socioeconomic factors, the baseline prediction tool performed well in the Brazilian cohort, demonstrating adequate discrimination and calibration. This supports its reliability in diverse settings.

In-situ calibration for real-time infrasound station monitoring and improved wave parameter determination

Infrasound signals can be detected using a time-delay of arrival approach to derive the back azimuth and trace velocity of the coherent wave. For these calculations, it is necessary to have a calibrated measure of the pressure. Although the calibration of microbarometers can be performed in a laboratory setting and in the field with specific metrological means such as those developed by the CEA, it is much more difficult to determine the transfer function of the wind noise reduction systems (WNRS), designed to reduce the wind associated noise. In-situ calibration of these WNRS's can be performed using a co-located, calibrated reference sensor. System defects, such as flooded pipes or blocked inlets, have significant impacts on the response, which in turn would influence the calculated infrasonic wave parameters. These defects can be characterized using in-situ calibration measurements. Experiments were undertaken at the infrasound station IS26 as part of the EMPIR Infra-AUV project, using a temporary detector that was used to simulate defects on the WNRS. This allowed for the effects on real infrasound detections to be quantified and corrected using in-situ calibrations. Additionally, case studies at IMS stations were performed demonstrating the utility of these methods on operational infrasound stations.