Estimation Of Standard Deviation Research Articles

ACETABULAR FRACTURES IN OLDER PATIENTS INTERVENTION TRIAL (ACEFIT): A FEASIBILITY TRIPLE-ARM RANDOMIZED CONTROLLED STUDY

Current treatment options for displaced acetabular fractures in elderly patients include non-surgical management, surgical fixation and surgical fixation with simultaneous hip replacement, the so-called “fix-and-replace”™. There remains a paucity of evidence to guide surgeons in decision making for these difficult injuries. The aim of this study was to assess the feasibility of performing an appropriately powered RCT between treatment options for acetabular fractures in older patients.This was an NIHR funded feasibility triple-arm RCT with participation from 7 NHS MTCs. Patients older than 60 were recruited if they had an acetabular fracture deemed sufficiently displaced for the treating surgeon to consider surgical fixation. Randomisation was performed on a 1:1:1 basis. The three treatment arms were non-surgical management, surgical fixation and fix-and-replace. Feasibility was assessed by willingness of patients to participate and clinicians to recruit, drop out rate, estimates of standard deviation to inform the sample size calculation for the full trial and completion rates to inform design of a future definitive trial. EQ-5D was the primary outcome measure at 6 months, OHS and Disability Rating Index were secondary outcome measures.Of 117 eligible patients, 60 were randomised whilst 50 declined study participation. Nine patients did not receive their allocated intervention. Analysis was performed on an intention to treat basis. During the study period 4 patients withdrew before final review, 4 patients died and 1 was lost to follow-up. The estimated sample size for a full scale study was calculated to be 1474 participants for an EQ-5D MCID of 0.06 with a power of 0.8.This feasibility study suggests a full scale trial would require international collaboration. This study also has provided observed safety data regarding mortality and morbidity for the fix-and-replace procedure to aid surgeons in the decision-making process when considering treatment options.

Прогнозування динаміки розвитку тваринництва за допомогою часових рядів

The construction of time series using historical data is one of the urgent problems of management in the agrarian sector, since the analysis and forecasting of processes related to the food security of the State, of region, and of economic entities is crucial. With the help of forecasts, businesses can adjust their production activities in such a way as to meet demand and deliver products to consumers on time. The aim of this study is to forecast the dynamics of the development of stock of cattle and cows and to determine the optimal forecasting period. For this type of analysis, statistical methods related to autoregression are used: autoregressive models, moving average models, or combinations of both; integrated models with a variable structure, and models that include seasonal effects and exogenous factors with an autoregressive and moving average component in the model. Monthly statistics on the number of cattle and cows are provided: mean, standard deviation, minimum and maximum values, asymmetry and excess. The dynamics of decline in the number of stock of cattle and cows is shown. The studied series were tested for stationarity. To the time series of the number of cattle, the Box–Cox transformation was applied. The optimal parameters of the models used are presented. Forecast values for time intervals (months) have been obtained and changes in the number of stock of cattle over the past 17 years have been analyzed. The built time series are compared with the actual values, which is illustrated in the graphs. Estimates of standard deviation, mean absolute error for different forecasting terms are provided. When comparing these estimates for different time intervals, the optimal time period for the forecast (24 months) was determined. This study allows farms and enterprises of the industry to realize how much products (milk, meat) can be harvested or obtained in the future. This assists in taking the necessary managerial steps: plan for resource needs, improve efficiency, increase profits, reduce costs, and adapt to changes in the market.

A mixed method evaluation of a theory based intervention to reduce sedentary behaviour in contact centres- the stand up for health stepped wedge feasibility study

Introduction Contact centres have higher levels of sedentary behaviour than other office-based workplaces. Stand Up for Health (SUH) is a theory-based intervention developed using the 6SQuID framework to reduce sedentary behaviour in contact centre workers. The aim of this study was to test acceptability and feasibility of implementing SUH in UK contact centres. Methods The study was conducted in 2020–2022 (pre COVID and during lockdown) and used a stepped-wedge cluster randomised trial design including a process evaluation. The intervention included working with contact centre managers to develop and implement a customised action plan aligning with SUH’s theory of change. Workplace sedentary time, measured using activPAL™ devices, was the primary outcome. Secondary outcomes included productivity, mental wellbeing, musculoskeletal health and physical activity. Empirical estimates of between-centre standard deviation and within-centre standard deviation of outcomes from pre-lockdown data were calculated to inform sample size calculations for future trials. The process evaluation adopted the RE-AIM framework to understand acceptability and feasibility of implementing the intervention. Interviews and focus groups were conducted with contact centre employees and managers, and activity preferences were collected using a questionnaire. Results A total of 11 contact centres participated: 155 employees from 6 centres in the pre-lockdown data collection, and 54 employees from 5 centres post-lockdown. Interviews and focus groups were conducted with 33 employees and managers, and 96 participants completed an intervention activity preference questionnaire. Overall, the intervention was perceived as acceptable and feasible to deliver. Most centres implemented several intervention activities aligned with SUH’s theory of change and over 50% of staff participated in at least one activity (pre-lockdown period). Perceived benefits including reduced sedentary behaviour, increased physical activity, and improved staff morale and mood were reported by contact centre employees and managers. Conclusions SUH demonstrates potential as an appealing and acceptable intervention, impacting several wellbeing outcomes. Trial registration The trial has been registered on the ISRCTNdatabase: http://www.isrctn.com/ISRCTN11580369.

Estimating individual health-related quality of life changes in low back pain patients

BackgroundThere is a need to evaluate different options for estimating individual change in health-related quality of life for patients with low back pain.MethodsSecondary analysis of data collected at baseline and 6 weeks later in a randomized trial of 749 adults with low back pain receiving usual medical care (UMC) or UMC plus chiropractic care at a small hospital at a military training site or two large military medical centers. The mean age was 31; 76% were male and 67% were White. The study participants completed the Patient-Reported Outcomes Measurement Information System (PROMIS®)-29 v 1.0 physical function, pain interference, pain intensity, fatigue, sleep disturbance, depression, anxiety, satisfaction with participation in social roles, physical summary, and mental health summary scores (T-scored with mean = 50 and standard deviation (SD) = 10 in the U.S. general population).ResultsReliability estimates at the baseline ranged from 0.700 to 0.969. Six-week test–retest intraclass correlation estimates were substantially lower than these estimates: the median test–retest intraclass correlation for the two-way mixed-effects model was 0. 532. Restricting the test–retest reliability estimates to the subset who reported they were about the same as at baseline on a retrospective rating of change item increased the median test–retest reliability to 0.686. The amount of individual change that was statistically significant varied by how reliability was estimated, and which SD was used. The smallest change needed was found when internal consistency reliability and the SD at baseline were used. When these values were used, the amount of change needed to be statistically significant (p < .05) at the individual level ranged from 3.33 (mental health summary scale) to 12.30 (pain intensity item) T-score points.ConclusionsWe recommend that in research studies estimates of the magnitude of individual change needed for statistical significance be provided for multiple reliability and standard deviation estimates. Whenever possible, patients should be classified based on whether they 1) improved significantly and perceived they got better, 2) improved significantly but did not perceive they were better, 3) did not improve significantly but felt they got better, or 4) did not improve significantly or report getting better.

Higher-order asymptotic corrections and their application to the Gamma Variance Model

We present improved methods for calculating confidence intervals and p values in situations where standard asymptotic approaches fail due to small sample sizes. We apply these techniques to a specific class of statistical model that can incorporate uncertainties in parameters that themselves represent uncertainties (informally, “errors on errors”) called the Gamma Variance Model. This model contains fixed parameters, generically denoted by ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon $$\\end{document}, that represent the relative uncertainties in estimates of standard deviations of Gaussian distributed measurements. If the ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon $$\\end{document} parameters are small, one can construct confidence intervals and p values using standard asymptotic methods. This is formally similar to the familiar situation of a large data sample, in which estimators for all adjustable parameters have Gaussian distributions. Here we address the important case where the ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon $$\\end{document} parameters are not small and as a consequence the first-order asymptotic distributions do not represent a good approximation. We investigate improved test statistics based on the technology of higher-order asymptotics (modified likelihood root and Bartlett correction). The effective application of higher-order corrections removes an important computational barrier to the use of the Gamma Variance Model.

Estimation of site-specific multivariate probability distribution of soil properties using a mixed sampling technique

Estimation of multivariate probability density function (PDF) of soil properties based on limited site investigation data plays an important role in data driven-based geotechnical engineering analysis and design. However, this estimation is often intractable because the site-specific investigation data generally are multivariate, uncertain and unique, sparse, incomplete, sometimes corrupt and spatially variable. The estimation is more challenging when the uncertainties in model parameters of the distribution (i.e., mean, standard deviations and scale of fluctuation of soil properties) are taken into consideration. To address these difficulties, this study proposed a mixed sampling technique by combing the Gibbs sampler and transitional Markov Chain Monte Carlo sampler to effectively estimate the multivariate probability density function of soil properties. The proposed method is demonstrated through a virtual site example while the effect of statistical uncertainty in the scale of fluctuation on reliability analyses is illustrated in an infinite slope example. It is shown that the proposed method can not only handle the complex features of site investigation data but also effectively quantify the statistical uncertainties of the mean values, standard deviations and scale of fluctuation of the investigated soil properties. Ignoring the statistical uncertainties of scale of fluctuation can affect the estimation of mean and standard deviations of the soil parameters. When considering the statistical uncertainty of scale of fluctuation, the 95% confidence intervals for the profiles of the soil properties can rationally cover the actual soil data at the unobserved points. Ignoring the statistical uncertainty of scale of fluctuation can lead to the underestimation of the probability of failure of the infinite slope.

Automatic chest computed tomography image noise quantification using deep learning

PurposeThis study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions. MethodsA comprehensive phantom CT dataset (three dose levels, six reconstruction methods, amounting to 9240 slices) was acquired and used to train a convolutional neural network (CNN) to output an estimate of local image noise standard deviations (SD) from a single CT scan input. The CNN model consisting of seven convolutional layers was trained on the phantom image dataset representing a range of scan parameters and was tested with phantom images acquired in a variety of different scan conditions, as well as publicly available chest CT images to produce clinical noise SD maps. ResultsNoise SD maps predicted by the CNN agreed well with the ground truth both visually and numerically in the phantom dataset (errors of < 5 HU for most scan parameter combinations). In addition, the noise SD estimates obtained from clinical chest CT images were similar to running-average based reference estimates in areas without prominent tissue interfaces. ConclusionsPredicting local noise magnitudes without the need for repeated scans is feasible using DL. Our implementation trained with phantom data was successfully applied to open-source clinical data with heterogeneous tissue borders and textures. We suggest that automatic DL noise mapping from clinical patient images could be used as a tool for objective CT image quality estimation and protocol optimization.

Proposed Capability Indices Based on Robust Estimation Compared with Classical Capability Indices

A process capability study is a scientific and systematic procedure that uses control charts to detect and eliminate the unnatural causes of variation until a state of statistical control is reached. On the other hand, in order to meet the quality requirements of the final product, quality should be achieved at every stage of production. Another way of achieving good quality during production is to use statistical techniques at every stage of production. The purpose of this research is to apply it to process capacity indices in replace of the standard deviation estimator. The information, which is taken from the Coca-Cola/Erbil production process, illustrates the qualities of the beverage (750 ml). A Coca-Cola product's 100 observations are divided into 25 models. Employed both the standard deviation estimator-based and the robust Downton estimation-based process capability indices. It was determined that in this inquiry, the robust Downton estimation had better qualities than the standard division estimator because the robust Downton estimation process capacity index values were greater.

A statistical approach to large-scale group decision making with incomplete hesitant fuzzy linguistic information by incorporating risk attitude

In response to the lack of information and the arguments among the decision-makers (DMs) in large-scale group decision making (LSGDM), this study proposes a method that integrates decision risk and risk attitude into LSGDM with hesitant fuzzy linguistic preference relations (HFLPRs) based on statistical estimation. Firstly, the DMs are arranged into several subgroups via their risk attitudes. Next, the whole or part of the five-number summary for subgroups from incomplete information provided by DMs are collected. After that, the optimization model and Monte Carlo simulation are utilized to obtain mean and standard deviation estimators of the decision information, and the mean estimator is regarded as the evaluation information of subgroup. Then, the hesitant fuzzy linguistic term sets (HFLTSs) with different number of linguistic variables are normalized according to their risk attitudes and confidence interval. Meanwhile, the number of members in subgroups and the decision risk level of subgroups are combined to determine the weights of subgroups. Moreover, a minimum deviation model is derived to rank alternatives. Finally, a case study, sensitivity analysis and comparison are used to guarantee the rationality and availability of the proposed method.

The GMAO Hybrid 4D-EnVar Observing System Simulation Experiment Framework

Abstract This work describes the extension of the Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework to use a hybrid four-dimensional ensemble–variational (4D-EnVar) scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short intercomparison of upgrading from a 3D to a 4D OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify particular features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

Random noise test of analog-to-digital converters

The random noise test of analog to digital converters recommended by the IEEE 1057 Standard for digitizing waveform recorders is studied. The heuristically derived expression presented for the estimation of the random noise standard deviation is experimentally validated. The standard suggests a triangular stimulus signal. Here we will show how to use a sinusoidal stimulus signal to carry out the test. The influence of stimulus signal offset and amplitude on the estimation error is also analyzed and an expression is presented to compute the minimum amplitude value that guarantees an upper bound on the estimator bias.

Validity of the hydrogen ion mobilisation model during haemodialysis with time-dependent dialysate bicarbonate concentrations.

The hydrogen ion (H+) mobilisation model has been previously shown to accurately describe blood bicarbonate (HCO3) kinetics during haemodialysis (HD) when the dialysate bicarbonate concentration ([HCO3]) is constant throughout the treatment. This study evaluated the ability of the H+ mobilization model to describe blood HCO3 kinetics during HD treatments with a time-dependent dialysate [HCO3]. Data from a recent clinical study where blood [HCO3] was measured at the beginning of and every hour during 4-h treatments in 20 chronic, thrice-weekly HD patients with a constant (Treatment A), decreasing (Treatment B) and increasing (Treatment C) dialysate [HCO3] were evaluated. The H+ mobilization model was used to determine the model parameter (Hm) that provided the best fit of the model to the clinical data using nonlinear regression. A total of 114 HD treatments provided individual estimates of Hm. Mean ± standard deviation estimates of Hm during Treatments A, B and C were 0.153 ± 0.069, 0.180 ± 0.109 and 0.205 ± 0.141 L/min (medians [interquartile ranges] were 0.145 [0.118,0.191], 0.159 [0.112,0.209], 0.169 [0.115,0.236] L/min), respectively; these estimates were not different from each other (p = 0.26). The sum of squared differences between the measured blood [HCO3] and that predicted by the model were not different during Treatments A, B and C (p = 0.50), suggesting a similar degree of model fit to the data. This study supports the validity of the H+ mobilization model to describe intradialysis blood HCO3 kinetics during HD with a constant Hm value when using a time-dependent dialysate [HCO3].

The exponentially‐weighted control charts for geometrically moving averages

AbstractThis article is a sequel to the previous entitled “A Further Note on the Exponentially‐Weighted Moving Average Control Charts for Monitoring Gradual Shifts in a Process Mean” where the constant sample size n was equal to 1 in each subgroup. We have now generalized the results of the previous article first to the case of constant sample size n &gt; 1 in each subgroup, and then to varying sample sizes; although, the average run length for the most general case of varying sample sizes is a weighted approximation. In our Section 2, we are proposing an estimator of process standard deviation (STD) σX that is more accurate than all previously available in statistical literature only if the targeted average, μ0, is the required standard process mean.

On the Problems of Detecting Fast Radio Bursts with the LPA LPI

We present a verification of fast radio bursts (FRBs) previously published by V.A. Fedorova and A.E. Rodin. These FRBs were found in the monitoring data with the Large Phased Array (LPA) radio telescope using a search algorithm based on data convolution with a scattered pulse pattern. The same 6-channel data (channel width 415 kHz) were used for verification, in which FRBs were found with dispersion measures of 247, 570, and 1767 pc/cm3. An additional verification of the published FRBs was also carried out in 32‑channel data (channel width 78 kHz). We can not confirm any of the published FRBs on the signal-to-noise ratios claimed in the original paper. The main errors are caused by incorrect determination of the baseline and incorrect estimation of noise standard deviations.

On the Problems of Detecting Fast Radio Bursts with the LPA LPI

We present a verification of fast radio bursts (FRBs) previously published by V.A. Fedorova and A.E. Rodin. These FRBs were found in the monitoring data with the Large Phased Array (LPA) radio telescope using a search algorithm based on data convolution with a scattered pulse pattern. The same 6-channel data (channel width 415 kHz) were used for verification, in which FRBs were found with dispersion measures of 247, 570, and 1767 pc/cm3. An additional verification of the published FRBs was also carried out in 32‑channel data (channel width 78 kHz). We can not confirm any of the published FRBs on the signal-to-noise ratios claimed in the original paper. The main errors are caused by incorrect determination of the baseline and incorrect estimation of noise standard deviations.

Effect of Global Warming on the Yields of Strawberry in Queensland: A Mini-Review

Light, temperature and rainfall affect the growth and yield of strawberry plants (Fragaria × ananassa Duch.). The objective of this review was to determine the impact of global warming on the yields of strawberry in a temperate (summer crop) and subtropical environment (winter crop) in southern Queensland, Australia. Information was collected on the changes in temperature over five decades in two locations in this area. The relationship between relative yield and temperature from published data was used to determine the impact of global warming on productivity in the two locations. Finally, the impact of elevated concentrations of CO2 and temperature on yield was examined from studies in the literature. The average daily mean temperature has increased by 2 °C over the season on the Sunshine Coast (winter crop) since 1967 (p &lt; 0.001, R2 = 0.69). The impact of global warming has been less severe on the Granite Belt (summer crop), with a 1 °C increase in temperature (p &lt; 0.001, R2 = 0.37). Information was collected from the literature on the yield in individual temperature regimes in an experiment and these data were compared with the maximum yield in the same experiment (relative yield). There was a negative linear relationship between relative yield and temperature in most of the published literature. The mean (± s.d. or standard deviation) estimate of the slope from the regression was −0.14 (± 0.14), the median was −0.11 and the range was from −0.51 to 0.11 (n = 14 studies). Increases in temperature were associated with a decrease in yield of 14% to 28% in the two areas in Queensland. The results of other research indicated that elevated concentrations of CO2 do not benefit productivity when combined with elevated temperatures. Further decreases in yield are expected in the next few decades in the absence of heat-tolerant cultivars or other mitigating strategies.

Multilayer Perceptron and Bayesian Neural Network-Based Elastic Implicit Full Waveform Inversion

We introduce and analyze the elastic implicit full waveform inversion (EIFWI) of seismic data, which uses neural networks to generate elastic models and perform full waveform inversion. EIFWI carries out inversion by linking two main networks: a neural network that generates elastic models and a recurrent neural network to perform the modeling. The approach is distinct from conventional waveform inversion in two key ways. First, it reduces reliance on accurate initial models relative to conventional FWI. Instead, it invokes general information about the target area, for instance, estimates of means and standard deviations of medium properties in the target area or, alternatively, well-log information in the target area. Second, iterative updating directly affects the weights in the neural network rather than the elastic model. Elastic models can be generated in the first part of the EIFWI process in either of two ways: through the use of a multilayer perceptron (MLP) network or a Bayesian neural network (BNN). Numerical testing is suggestive that the MLP-based EIFWI approach in principle builds accurate models in the absence of an explicit initial model, and the BNN-based EIFWI can give the uncertainty analysis for the prediction results.

Assessments of some trace metals in water samples of nursery pond of Grass Carp (Ctenopharyngodon idella, Valenciennes, 1844) in Bannu Fish Hatchery of Khyber Pakhtunkhwa, Pakistan

Abstract The present investigation was aimed to examine the concentrations of trace metals including e.g copper (Cu), manganese (Mn), nickel (Ni), and zinc (Zn) in water samples collected from nursery pond of grass carp (Ctenopharyngodon idella) in Bannu Hatchery of Khyber Pakhtunkhwa during the period from April 2018 to January 2019. The temperature and pH of each water sample were measured for the whole study duration. The concentration of Copper (Cu), Manganese (Mn), Nickel (Ni), and Zinc (Zn) in collected water samples were measured in mg/liter by using Atomic Absorption Spectrophotometer. The blank and standard solutions for device calibration Standard solutions i.e., 2.0 mg, 4.0 mg, and 6.0 were used to measure the concentration of these metals in water samples to verify the measurements. The data was statistically analyzed on descriptive statistics (estimation of proportions and standard deviation) used to summarize mean concentration. The results obtained of both temperature and pH of water samples were found in ranged 10 to 36 0C and 7.0 to 8.44; whereas the size of fry stages was ranged from 4.0 to 56.0 mm in total length. The results of investigated metals found in pond water samples are in order of Zn&gt;Mn&gt;Ni&gt;Cu, respectively. As optimum temperature and pH for grass carp were mostly between 15 0C and 30 0C and pH 6.5 to 8.0. It was concluded from obtained results that temperature, pH, and trace metals were found appropriate for the growth of Ctenopharyngodon idella from fry to fingerling stages, but the highest amount of zinc can cause its mortality. It is a preliminary study on grass carp culturing in Bannu fish hatchery so, it would provide useful information for model fish seed production unit in a hatchery.

Modeling the Vertical Distribution of the Milky Way’s Flat Subsystem Objects

This paper is an initial stage of consideration of the general problem of joint modeling of the vertical structure of a Galactic flat subsystem and the average surface of the disk of the Galaxy, taking into account the natural and measurement dispersions. We approximate the average surface of the Galactic disk in the region covered by the data with a general (polynomial) model and determine its parameters by minimizing the squared deviations of objects along the normal to the model surface. The smoothness of the model, i.e., its order n, is optimized. An outlier elimination algorithm is applied. The developed method allows us to simultaneously identify significant details of the Galactic warping and estimate the offset z ☉ of the Sun relative to the average (in general, non-flat) surface of the Galactic disk and the vertical scale of the object system under consideration for an arbitrary area of the disk covered by data. The method is applied to data on classical Cepheids. Significant local extremes of the average disk surface model were found based on Cepheid data: the minimum in the first Galactic quadrant and the maximum in the second. A well-known warp (lowering of the disk surface) in the third quadrant has been confirmed. The optimal order of the model describing all these warping details was found to be n o = 4. The local (for a small neighborhood of the Sun, n o = 0) estimate of pc is close to the non-local (taking into account warping, n o = 4) pc (statistical and calibration uncertainties are indicated), which suggests that the proposed modeling method eliminates the influence of warping on the z ☉ estimate. However, the non-local estimate of the vertical standard deviation of Cepheids pc differs significantly from the local pc, which implies the need to introduce more complex models for the vertical distribution outside the Sun’s vicinity.

Метод оценки радиационного риска медицинского облучения при прохождении компьютерной томографии детей и подростков на основе данных протоколов сканирования

Computed tomography (CT) scan is a fairly accurate and reliable method for diagnosing various diseases, however, based on irradiation of the patient with ionizing radiation, it can lead to an excess risk of developing malignant neoplasms. According to the safety standards of the International Atomic Energy Agency, no patient can be exposed to medical radiation unless he or she has been informed of the risks associated with radiation exposure. This requirement is also contained in the Russian Radiation Safety Standards (NRB-99/2009). Radiation risk is estimated on the basis of calculated values of equivalent doses in the patient's organs and tissues using radiation risk models of the International Commission on Radiological Protection. This article is devoted to the improvement of the method for calculating equivalent doses in the organs and tissues of patients with computed tomography scan for further assessment of radiation risks. For 15 modern computed tomography scanners, with different geometry of patient scanning, the data necessary for calculating the equivalent doses in organs and tissues based on the parameters of the children's phantom were updated. The coefficients of conversion of the surrogate measure of the patient's radiation dose for the entire CT scan study, namely, the value of the "Dose Length Product" (DLP), into the values of equivalent doses in individual organs and tissues exposed to radiation were determined. The results of the calculation are given for five typical scan areas: chest, abdomen, pelvis, head and neck. In the course of simulation numerical modeling, organs and tissues with the highest equivalent doses were determined during CT scan examinations of children and adolescents. Unlike adult patients, for children and adolescents, the dose load on the surface of the bone is significantly lower and does not stand out in any of the considered types of CT scan examinations. It should be noted that with CT scan of the cervical spine, due to the specifics of the geometry of scanning new types of tomography scanners, the dose load on the thyroid gland of boys increased. The dose conversion factors obtained in this paper with estimates of standard deviations make it possible to assess the radiation risks of children and adolescents during examinations on modern CT scanners and optimize their dose loads.