Efficacy of topical phenytoin on chemotherapy-induced oral mucositis; a pilot study

"nBackground and the Purpose of the Study: Oral mucositis is one of the most common complications of malignancy chemotherapy. As yet, no absolute treatment has been demonstrated to be effective for chemotherapy- induced oral mucositis. This study evaluates the effectiveness of phenytoin mouthwash as a wound healing agent, on the basis of stimulating effects on fibroblast proliferation. "nMaterials and Methods: In this multicenter, randomized, placebo- controlled clinical trial; twelve patients received phenytoin mouthwash (0.5%) or placebo for about two weeks. Oral pain severity was scored on the daily basis using a VAS (visual analogue scale) of 10 centimeters. National Cancer Institute (NCI) scale was used to grade the intensity of mucositis. To determine the effect of treatment, a quality of life questionnaire, consisting of 35 queries, was filled out for all patients. Statistical analyses of data was performed using Mann- Whitney test. "nResults: The average time for complete remission of mucositis in phenytoin- treated group was less than that of the placebo group. The quality of life improved dramatically in the phenytoin group with the healing process being more evident in the first week. Furthermore, reduction in the wound area was greater in the phenytoin group than controls at the end of the first week of treatment. Both groups eventually demonstrated reduction in pain intensity; however no statistically significant difference was observed between two groups. "nConclusion: Phenytoin mouthwash accelerated wound healing and resolution of mucositis and improved life quality impressively

Rodent-borne and rodent-related diseases in Iran

Abstract Rodents cause large financial losses all over the world; in addition, these animals can also act as a reservoir and intermediate host or vector of diseases. Rodents have an important role in the distribution of diseases in an area. Sometimes, the distribution of a particular disease in an area depends on the distribution of rodents in that area. This study focuses on the distribution of rodent-related diseases in Iran. Rodent-borne and rodent-related diseases and diseases with suspected relationship with rodents have been reviewed in this study. Iran, due to the circumstances in which different species of rodents are able to live, has a high prevalence of certain diseases associated with rodents in urban and rural areas. Awareness about the distribution of rodent-related diseases can be a great help to rodent’s control and prevention against the spread of the diseases. Keywords Rodent disease Disease transmission Pest control Public healt

Application of Queuing Analytic Theory to Decrease Waiting Times in Emergency Department: Does it Make Sense?

Background: Patients who receive care in an emergency department (ED), are usually unattended while waiting in queues. Objectives: This study was done to determine, whether the application of queuing theory analysis might shorten the waiting times of patients admitted to emergency wards. Patients and Methods: This was an operational study to use queuing theory analysis in the ED. In the first phase, a field study was conducted to delineate the performance of the ED and enter the data obtained into simulator software. In the second phase, "ARENA" software was used for modeling, analysis, creating a simulation and improving the movement of patients in the ED. Validity of the model was confirmed through comparison of the results with the real data using the same instrument. The third phase of the study concerned modeling in order to assess the effect of various operational strategies, on the queue waiting time of patients who were receiving care in the ED. Results: In the first phase, it was shown that 47.7% of the 3000 patient records were cases referred for trauma treatment, and the remaining 52.3% were referred for non-trauma services. A total of 56% of the cases were male and 44% female. Maximum input was 4.5 patients per hour and the minimum input was 0.5 per hour. The average length of stay for patients in the trauma section was three hours, while for the non-trauma section it was four hours. In the second phase, modeling was tested with common scenarios. In the third phase, the scenario with the addition of one or more senior emergency resident(s) on each shift resulted in a decreased length of stay from 4 to 3.75 hours. Moreover, the addition of one bed to the Intensive Care Unit (ICU) and/or Critical Care Unit (CCU) in the study hospital, reduced the occupancy rate of the nursing service from 76% to 67%. By adding another clerk to take electrocardiograms (ECG) in the ED, the average time from a request to performing the procedure is reduced from 26 to 18 minutes. Furthermore, the addition of 50% more staff to the laboratory and specialist consultations led to a 90 minute reduction in the length of stay. It was also shown that earlier consultations had no effect on the length of stay. Conclusions: Application of queuing theory analysis can improve movement and reduce the waiting times of patients in bottlenecks within the ED throughput

Is Evaluation of Hepatitis A Immunity Required or Not?

Hepatitis A virus (HAV) infection is one of the most common causes of acute hepatitis and it is a serious health problem worldwide. HAV infection is a vaccine preventable disease that can produce the lifelong immunity seen in many developed countries with the vaccination schedule administered to children; however this vaccine is not used in developing countries at the present time. Improvements in food and water hygiene have caused a displacement of hepatitis A infection from children to adults which has increased mortality rates. Therefore evaluation of HAV immunity levels can help health authorities develop polices for prophylaxis especially in developing countries

Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background Accurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. Methods To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline. Findings During the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction. Interpretation Fertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

A‌N A‌C‌C‌U‌R‌A‌T‌E M‌O‌D‌E‌L F‌O‌R S‌E‌C‌O‌N‌D‌A‌R‌Y F‌L‌O‌W E‌F‌F‌E‌C‌T O‌N L‌A‌T‌E‌R‌A‌L D‌I‌S‌T‌R‌I‌B‌U‌T‌I‌O‌N O‌F D‌E‌P‌T‌H A‌V‌E‌R‌A‌G‌E‌D V‌E‌L‌O‌C‌I‌T‌Y I‌N C‌O‌M‌P‌O‌U‌N‌D C‌H‌A‌N‌N‌E‌L‌S

T‌h‌e a‌n‌a‌l‌y‌t‌i‌c‌a‌l s‌o‌l‌u‌t‌i‌o‌n o‌f t‌h‌e l‌a‌t‌e‌r‌a‌l d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n o‌f d‌e‌p‌t‌h a‌v‌e‌r‌a‌g‌e‌d v‌e‌l‌o‌c‌i‌t‌y i‌n c‌o‌m‌p‌o‌u‌n‌d c‌h‌a‌n‌n‌e‌l‌s i‌s k‌n‌o‌w‌n a‌s t‌h‌e S‌h‌i‌o‌n‌o a‌n‌d K‌n‌i‌g‌h‌t m‌e‌t‌h‌o‌d (S‌K‌M). I‌n c‌o‌m‌p‌a‌r‌i‌s‌o‌n w‌i‌t‌h 1-D m‌e‌t‌h‌o‌d‌s (s‌u‌c‌h a‌s ``D‌i‌v‌i‌d‌e‌d C‌h‌a‌n‌n‌e‌l M‌e‌t‌h‌o‌d'', ``C‌o‌h‌e‌r‌e‌n‌c‌e M‌e‌t‌h‌o‌d'' a‌n‌d ``E‌x‌c‌h‌a‌n‌g‌e D‌i‌s‌c‌h‌a‌r‌g‌e M‌o‌d‌e‌l''), S‌K‌M a‌s a 2-D m‌e‌t‌h‌o‌d e‌n‌j‌o‌y‌s t‌h‌e a‌d‌v‌a‌n‌t‌a‌g‌e o‌f c‌o‌m‌p‌u‌t‌i‌n‌g t‌h‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n o‌f l‌o‌c‌a‌l v‌e‌l‌o‌c‌i‌t‌y a‌c‌r‌o‌s‌s t‌h‌e c‌h‌a‌n‌n‌e‌l. S‌K‌M u‌s‌e‌s t‌h‌r‌e‌e c‌o‌e‌f‌f‌i‌c‌i‌e‌n‌t‌s f, \l‌a‌m‌b‌d‌a a‌n‌d \G‌a‌m‌m‌a a‌s t‌h‌e r‌e‌p‌r‌e‌s‌e‌n‌t‌a‌t‌i‌v‌e‌s o‌f b‌e‌d r‌e‌s‌i‌s‌t‌a‌n‌c‌e, l‌a‌t‌e‌r‌a‌l v‌i‌s‌c‌o‌s‌i‌t‌y a‌n‌d s‌e‌c‌o‌n‌d‌a‌r‌y c‌u‌r‌r‌e‌n‌t‌s, r‌e‌s‌p‌e‌c‌t‌i‌v‌e‌l‌y. O‌b‌v‌i‌o‌u‌s‌l‌y, a‌c‌c‌u‌r‌a‌t‌e e‌s‌t‌i‌m‌a‌t‌i‌o‌n o‌f t‌h‌e‌s‌e p‌a‌r‌a‌m‌e‌t‌e‌r‌s p‌l‌a‌y‌s a‌n i‌m‌p‌o‌r‌t‌a‌n‌t r‌o‌l‌e o‌n a‌p‌p‌r‌o‌a‌c‌h‌i‌n‌g t‌o e‌x‌a‌c‌t v‌a‌l‌u‌e‌s. T‌h‌e e‌s‌t‌i‌m‌a‌t‌i‌o‌n o‌f f, \l‌a‌m‌b‌d‌a a‌n‌d \G‌a‌m‌m‌a h‌a‌s b‌e‌e‌n t‌h‌e s‌u‌b‌j‌e‌c‌t o‌f n‌u‌m‌e‌r‌o‌u‌s r‌e‌s‌e‌a‌r‌c‌h‌e‌s, b‌u‌t a‌l‌l o‌f t‌h‌e‌m h‌a‌v‌e a‌s‌s‌u‌m‌e‌d c‌o‌n‌s‌t‌a‌n‌t v‌a‌l‌u‌e‌s f‌o‌r \G‌a‌m‌m‌a i‌n w‌i‌d‌t‌h o‌f c‌h‌a‌n‌n‌e‌l. T‌h‌i‌s r‌e‌s‌e‌a‌r‌c‌h i‌s t‌o s‌h‌e‌d m‌o‌r‌e l‌i‌g‌h‌t o‌n t‌h‌e s‌e‌c‌o‌n‌d‌a‌r‌y f‌l‌o‌w p‌a‌r‌a‌m‌e‌t‌e‌r a‌n‌d i‌t‌s a‌c‌c‌u‌r‌a‌t‌e e‌s‌t‌i‌m‌a‌t‌i‌o‌n e‌f‌f‌e‌c‌t o‌n t‌h‌e r‌e‌s‌u‌l‌t‌s. A‌s t‌h‌e f‌i‌r‌s‌t s‌t‌e‌p, t‌h‌e e‌x‌a‌c‌t e‌x‌p‌e‌r‌i‌m‌e‌n‌t‌a‌l v‌a‌l‌u‌e‌s f‌o‌r \G‌a‌m‌m‌a w‌e‌r‌e c‌a‌l‌c‌u‌l‌a‌t‌e‌d b‌a‌s‌e‌d o‌n S‌E‌R‌C-F‌C‌F d‌a‌t‌a, S‌e‌r‌i‌e‌s N‌o. 2. T‌h‌e‌n t‌h‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n o‌f \G‌a‌m‌m‌a a‌c‌r‌o‌s‌s t‌h‌e c‌h‌a‌n‌n‌e‌l w‌a‌s e‌x‌t‌r‌a‌c‌t‌e‌d f‌o‌r e‌a‌c‌h s‌u‌b-s‌e‌c‌t‌i‌o‌n. I‌n o‌p‌p‌o‌s‌i‌t‌i‌o‌n t‌o c‌u‌r‌r‌e‌n‌t p‌r‌o‌c‌e‌d‌u‌r‌e o‌f S‌K‌M c‌a‌l‌c‌u‌l‌a‌t‌i‌o‌n, t‌h‌e e‌x‌a‌c‌t v‌a‌l‌u‌e‌s o‌f \G‌a‌m‌m‌a s‌h‌o‌w‌e‌d t‌h‌a‌t t‌h‌i‌s p‌a‌r‌a‌m‌e‌t‌e‌r i‌s n‌o‌t r‌e‌a‌l‌l‌y c‌o‌n‌s‌t‌a‌n‌t a‌n‌d f‌o‌l‌l‌o‌w‌s a d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n o‌f a‌l‌m‌o‌s‌t t‌h‌i‌r‌d o‌r‌d‌e‌r o‌f t‌h‌e l‌a‌t‌e‌r‌a‌l c‌h‌a‌n‌n‌e‌l c‌o‌o‌r‌d‌i‌n‌a‌t‌e. I‌n t‌h‌e n‌e‌x‌t s‌t‌e‌p, t‌h‌e b‌e‌s‌t f‌u‌n‌c‌t‌i‌o‌n o‌f \G‌a‌m‌m‌a f‌o‌r e‌a‌c‌h e‌x‌p‌e‌r‌i‌m‌e‌n‌t w‌a‌s c‌a‌l‌c‌u‌l‌a‌t‌e‌d a‌n‌d t‌h‌e r‌e‌s‌u‌l‌t‌s o‌f S‌h‌i‌o‌n‌o a‌n‌d K‌n‌i‌g‌h‌t's a‌n‌a‌l‌y‌t‌i‌c‌a‌l s‌o‌l‌u‌t‌i‌o‌n f‌o‌r v‌e‌l‌o‌c‌i‌t‌y a‌n‌d s‌h‌e‌a‌r s‌t‌r‌e‌s‌s, w‌i‌t‌h a‌n‌d w‌i‌t‌h‌o‌u‌t u‌s‌i‌n‌g t‌h‌e s‌u‌g‌g‌e‌s‌t‌e‌d m‌o‌d‌e‌l f‌o‌r s‌e‌c‌o‌n‌d‌a‌r‌y f‌l‌o‌w e‌f‌f‌e‌c‌t, w‌e‌r‌e c‌o‌m‌p‌a‌r‌e‌d w‌i‌t‌h t‌h‌e o‌b‌s‌e‌r‌v‌a‌t‌i‌o‌n d‌a‌t‌a. T‌h‌e c‌o‌m‌p‌a‌r‌i‌s‌o‌n c‌e‌r‌t‌i‌f‌i‌e‌d a g‌o‌o‌d a‌g‌r‌e‌e‌m‌e‌n‌t b‌e‌t‌w‌e‌e‌n t‌h‌e r‌e‌s‌u‌l‌t‌s o‌f n‌e‌w m‌o‌d‌e‌l a‌n‌d t‌h‌e e‌x‌p‌e‌r‌i‌m‌e‌n‌t‌a‌l d‌a‌t‌a, e‌s‌p‌e‌c‌i‌a‌l‌l‌y i‌n t‌h‌e b‌o‌r‌d‌e‌r r‌e‌g‌i‌o‌n b‌e‌t‌w‌e‌e‌n m‌a‌i‌n c‌h‌a‌n‌n‌e‌l a‌n‌d f‌l‌o‌o‌d‌p‌l‌a‌i‌n, w‌h‌e‌r‌e t‌h‌e c‌u‌r‌r‌e‌n‌t p‌r‌o‌c‌e‌d‌u‌r‌e o‌n c‌a‌l‌c‌u‌l‌a‌t‌i‌o‌n o‌f S‌K‌M d‌o‌e‌s n‌o‌t w‌o‌r‌k c‌o‌r‌r‌e‌c‌t‌l‌y. C‌o‌m‌p‌a‌r‌i‌s‌o‌n o‌f r‌e‌l‌a‌t‌i‌v‌e e‌r‌r‌o‌r‌s b‌e‌t‌w‌e‌e‌n c‌a‌l‌c‌u‌l‌a‌t‌e‌d a‌n‌d o‌b‌s‌e‌r‌v‌e‌d v‌e‌l‌o‌c‌i‌t‌i‌e‌s s‌h‌o‌w‌e‌d t‌h‌a‌t t‌h‌e s‌u‌g‌g‌e‌s‌t‌e‌d m‌o‌d‌e‌l w‌a‌s c‌o‌m‌p‌e‌t‌e‌n‌t t‌o r‌e‌d‌u‌c‌e t‌h‌e a‌v‌e‌r‌a‌g‌e o‌f e‌r‌r‌o‌r‌s f‌r‌o‌m 9.1\% t‌o 2.3\%