BACKGROUND: A new quality assurance and control method for electrometers using a new current source, different from the method published in the guidelines for electrometers, has been reported. This current source uses dry batteries and exhibits excellent performance in terms of voltage, temperature, and time characteristics. The electrometer sensitivity coefficient can be calculated by comparing the sensitivity of one electrometer with that of another on the electrometer calibration coefficient that has been calibrated by a calibration laboratory in advance in both methods. The guideline method requires two or more sets of ionization chambers and electrometers in the facility. In contrast, our method does not use ionization chambers; therefore, the sensitivity ratio of the electrometer can be measured in any facility. This study compared the uncertainty of the electrometer sensitivity factor calculated using the new current source method (current method) with that calculated using a linear accelerator (LINAC) and ionization chambers (LINAC method) described in the electrometer guidelines.
METHODS: In this study, we used a current source that we invented previously by Kawaguchi Electric Works in Japan. The sensitivity ratios of the electrometers were measured with three manufacture\'s electrometers. The electrometer sensitivity factor was calculated by multiplying the electrometer calibration coefficient. The ionization chamber was 30013 (PTW), and the current source was the current obtained from 10 MV TrueBeam X-rays under calibration conditions. The mean value, standard deviation, and coefficient of variation were calculated. The time required to set up the ionization chamber for calculating the sensitivity ratio of the electrometer was also measured. The accuracy was confirmed by calculating the expanded uncertainty of the electrometer sensitivity coefficients.
RESULTS: The LINAC method had a maximum coefficient of variation of 0.072%. The gross time of the LINAC method was approximately 110 min. The current method had a maximum coefficient of variation of 0.0055% and took less than half the time taken by the LINAC method (35 min) because there was no waiting time for the ionization chamber to be set up and the applied voltage to stabilize under calibration conditions. The expanded uncertainties of the electrometer calibration coefficients were 0.36% and 0.36%, respectively.
CONCLUSIONS: The new cross-comparison method for electrometer sensitivity factors using a current source is more efficient and useful than the linear accelerator method described in the guidelines; furthermore, this method ensured accuracy for quality assurance and control of electrometers.

The goal of metrological traceability is to have equivalent results for a measurand in clinical samples (CSs) irrespective of the in-vitro diagnostic medical device (IVD-MD) used for measurements. The International Standards Organization standard 17511 defines requirements for establishing metrological traceability of values assigned to calibrators, trueness control materials and human samples used with IVD-MDs. Each step in metrological traceability has an uncertainty associated with the value assigned to a material. The uncertainty at each step adds to the uncertainty from preceding steps such that the combined uncertainty gets larger at each step. The combined uncertainty for a CS result must fulfil an analytical performance specification (APS) for the maximum allowable uncertainty (umax CS). The umax CS can be partitioned among the steps in a metrological traceability calibration hierarachy to derive the APS for maximum allowable uncertainty at each step. Similarly, the criterion for maximum acceptable noncommutability bias can be derived from the umax CS. One of the challenges in determining if umax CS is fulfilled is determining the repeatability uncertainty (u Rw) from operating an IVD-MD within a clinical laboratory. Most of the current recommendations for estimating u Rw from internal quality control data do not use a sufficiently representative time interval to capture all relevant sources of variability in measurement results. Consequently, underestimation of u Rw is common and may compromise assessment of how well current IVD-MDs and their supporting calibration hierarchies meet the needs of clinical care providers.

OBJECTIVE: This work introduces the first assessment of CT calibration following the ESTRO\'s consensus guidelines and validating the HLUT through the irradiation of biological material.
METHODS: Two electron density phantoms were scanned with two CT scanners using two CT scan energies. The stopping power ratio (SPR) and mass density (MD) HLUTs for different CT scan energies were derived using Schneider\'s and ESTRO\'s methods. The comparison metric in this work is based on the Water-Equivalent Thickness (WET) difference between the treatment planning system and biological irradiation measurement. The SPR HLUTs were compared between the two calibration methods. To assess the accuracy of using MD HLUT for dose calculation in the treatment planning system, MD vs SPR HLUT was compared. Lastly, the feasibility of using a single SPR HLUT to replace two different energy CT scans was explored.
RESULTS: The results show a WET difference of less than 3.5% except for the result in the Bone region between Schneider\'s and ESTRO\'s methods. Comparing MD and SPR HLUT, the results from MD HLUT show less than a 3.5% difference except for the Bone region. However, the SPR HLUT shows a lower mean absolute percentage difference as compared to MD HLUT between the measured and calculated WET difference. Lastly, it is possible to use a single SPR HLUT for two different CT scan energies since both WET differences are within 3.5%.
CONCLUSIONS: This is the first report on calibrating an HLUT following the ESTRO\'s guidelines. While our result shows incremental improvement in range uncertainty using the ESTRO\'s guideline, the prescriptional approach of the guideline does promote harmonization of CT calibration protocols between different centres.

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In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus clustering outperforms native algorithms.
We extend here consensus clustering to allow for attribute weighting in the calculation of pairwise distances using existing regularized approaches. We propose a procedure for the calibration of the number of clusters (and regularization parameter) by maximizing the sharp score, a novel stability score calculated directly from consensus clustering outputs, making it extremely computationally competitive. Our simulation study shows better clustering performances of (i) approaches calibrated by maximizing the sharp score compared to existing calibration scores and (ii) weighted compared to unweighted approaches in the presence of features that do not contribute to cluster definition. Application on real gene expression data measured in lung tissue reveals clear clusters corresponding to different lung cancer subtypes.
The R package sharp (version ≥1.4.3) is available on CRAN at https://CRAN.R-project.org/package=sharp.

This document developed by the International Society for Clinical Electrophysiology of Vision (ISCEV) provides guidance for calibration and verification of stimulus and recording systems specific to clinical electrophysiology of vision. This guideline provides additional information for those using ISCEV Standards and Extended protocols and supersedes earlier Guidelines. The ISCEV guidelines for calibration and verification of stimuli and recording instruments (2023 update) were approved by the ISCEV Board of Directors 01, March 2023.

The Joint Committee for Traceability in Laboratory Medicine (JCTLM) currently lists the secondary commutable certified reference material (CRM) ERM DA-474/IFCC (DA-474) \"C-Reactive Protein in Human Serum\" and two generic immunoassay-based method principles as the basis for implementing the metrological traceability of C-reactive protein (CRP) measurements by end-user measurement procedures used by medical laboratories. The current metrological traceability has produced well harmonized results for clinical samples among different end-user measurement procedures. New higher-order pure substance and secondary commutable CRMs have been nominated for listing by the JCTLM. However, the data supporting performance of these new candidate CRMs, including use of new mass spectrometry based candidate reference measurement procedures (RMPs), was not clear regarding the influence that introducing these new CRMs would have on the current well harmonized results achieved with the existing metrological traceability to DA-474. The clinically relevant CRP measurand in blood serum or plasma is a pentamer of identical subunits, which adds complexity to the application of higher-order CRMs and RMPs. The JCTLM convened a workshop in December 2022 to review the appropriate implementation of metrological traceability of CRP measurements. The workshop consensus was that the extent-of-equivalence data must include considerations about the impact of a new CRM when used for its intended purpose in the calibration hierarchies of existing end-user measuring systems; and that a new RMP must compare results with another existing well validated candidate RMP or with a globally available end-user measurement system.

Studies have shown large variations in stopping-power ratio (SPR) prediction from computed tomography (CT) across European proton centres. To standardise this process, a step-by-step guide on specifying a Hounsfield look-up table (HLUT) is presented here.
The HLUT specification process is divided into six steps: Phantom setup, CT acquisition, CT number extraction, SPR determination, HLUT specification, and HLUT validation. Appropriate CT phantoms have a head- and body-sized part, with tissue-equivalent inserts in regard to X-ray and proton interactions. CT numbers are extracted from a region-of-interest covering the inner 70% of each insert in-plane and several axial CT slices in scan direction. For optimal HLUT specification, the SPR of phantom inserts is measured in a proton beam and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. Including both phantom inserts and tabulated human tissues increases HLUT stability. Piecewise linear regressions are performed between CT numbers and SPRs for four tissue groups (lung, adipose, soft tissue, and bone) and then connected with straight lines. Finally, a thorough but simple validation is performed.
The best practices and individual challenges are explained comprehensively for each step. A well-defined strategy for specifying the connection points between the individual line segments of the HLUT is presented. The guide was tested exemplarily on three CT scanners from different vendors, proving its feasibility.
The presented step-by-step guide for CT-based HLUT specification with recommendations and examples can contribute to reduce inter-centre variations in SPR prediction.

Fear conditioning is a widely used laboratory model to investigate learning, memory, and psychopathology across species. The quantification of learning in this paradigm is heterogeneous in humans and psychometric properties of different quantification methods can be difficult to establish. To overcome this obstacle, calibration is a standard metrological procedure in which well-defined values of a latent variable are generated in an established experimental paradigm. These intended values then serve as validity criterion to rank methods. Here, we develop a calibration protocol for human fear conditioning. Based on a literature review, series of workshops, and survey of N = 96 experts, we propose a calibration experiment and settings for 25 design variables to calibrate the measurement of fear conditioning. Design variables were chosen to be as theory-free as possible and allow wide applicability in different experimental contexts. Besides establishing a specific calibration procedure, the general calibration process we outline may serve as a blueprint for calibration efforts in other subfields of behavioral neuroscience that need measurement refinement.