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  1. Muhammad Nur Arsyad Azman, Ng, Choy Peng, Faridah Hanim Khairuddin, Neza Ismail, Wan Mohamed Syafuan Wan Sabri
    MyJurnal
    Road surface condition of a pavement is one of the most important features as it affect driving comfort and safety. A good road surface condition could reduce the risk of traffic accidents and injuries. Pavement Condition Index (PCI) is one of the important tools to measure the pavement performance. By conducting pavement evaluation, civil engineers could prioritize the maintenance and rehabilitation which usually incurred a huge cost. In University Pertahanan Nasional Malaysia (UPNM), there was no proper maintenance and rehabilitation scheduled for the roads as no performance evaluation tool available to measure the pavement condition. Thus, the objective of this study was to develop a Composite Pavement Performance Index (CPPI) to monitor the pavement condition and to rank the roads in UPNM. To develop the CPPI, road defects data were collected from 6 internal roads in UPNM. From the data collected, 4 major distresses were identified: longitudinal cracking, crocodile cracking, potholes and ravelling were found more likely to affect the pavement’s condition in UPNM. By measuring the growth of the distresses over a period of 6 months, modelling was conducted using simple linear regression. The growth of the distresses were compared, and odds ratios were computed to calculate the weightage of each distress for the determination of the CPPI value. The CPPI value developed could be used to rank the roads in UPNM. This study demonstrated that the road connecting to the library building in UPNM experienced the worst pavement deterioration with a PCI of 24 or a CPPI value of 1.1915. The level of severity was classified as “SERIOUS” in accordance to ASTM D6433. This road was recommended for reconstruction to increase the comfort and safety for road users
    Matched MeSH terms: Linear Models
  2. Tee HP, Corte C, Al-Ghamdi H, Prakoso E, Darke J, Chettiar R, et al.
    World J Gastroenterol, 2010 Aug 21;16(31):3905-10.
    PMID: 20712051
    AIM: To study the significance of cap-fitted colonoscopy in improving cecal intubation time and polyp detection rate.

    METHODS: This study was a prospective randomized controlled trial conducted from March 2008 to February 2009 in a tertiary referral hospital at Sydney. The primary end point was cecal intubation time and the secondary endpoint was polyp detection rate. Consecutive cases of total colonoscopy over a 1-year period were recruited. Randomization into either standard colonoscopy (SC) or cap-assisted colonoscopy (CAC) was performed after consent was obtained. For cases randomized to CAC, one of the three sizes of cap was used: D-201-15004 (with a diameter of 15.3 mm), D-201-14304 (14.6 mm) and D-201-12704 (13.0 mm). All of these caps were produced by Olympus Medical Systems, Japan. Independent predictors for faster cecal time and better polyp detection rate were also determined from this study.

    RESULTS: There were 200 cases in each group. There was no significant difference in terms of demographic characteristics between the two groups. CAC, when compared to the SC group, had no significant difference in terms of cecal intubation rate (96.0% vs 97.0%, P = 0.40) and time (9.94 +/- 7.05 min vs 10.34 +/- 6.82 min, P = 0.21), or polyp detection rate (32.8% vs 31.3%, P = 0.75). On the subgroup analysis, there was no significant difference in terms of cecal intubation time by trainees (88.1% vs 84.8%, P = 0.40), ileal intubation rate (82.5% vs 79.0%, P = 0.38) or total colonoscopy time (23.24 +/- 13.95 min vs 22.56 +/- 9.94 min, P = 0.88). On multivariate analysis, the independent determinants of faster cecal time were consultant-performed procedures (P < 0.001), male patients (P < 0.001), non-usage of hyoscine (P < 0.001) and better bowel preparation (P = 0.01). The determinants of better polyp detection rate were older age (P < 0.001), no history of previous abdominal surgery (P = 0.04), patients not having esophagogastroduodenoscopy in the same setting (P = 0.003), trainee-performed procedures (P = 0.01), usage of hyoscine (P = 0.01) and procedures performed for polyp follow-up (P = 0.01). The limitations of the study were that it was a single-center experience, no blinding was possible, and there were a large number of endoscopists.

    CONCLUSION: CAC did not significantly different from SC in term of cecal intubation time and polyp detection rate.

    Matched MeSH terms: Linear Models
  3. Zhu TH, Mooi CS, Shamsuddin NH, Mooi CS
    World J Diabetes, 2019 Jul 15;10(7):403-413.
    PMID: 31363387 DOI: 10.4239/wjd.v10.i7.403
    BACKGROUND: There are limited studies on diabetes empowerment among type 2 diabetes patients, particularly in the primary care setting.

    AIM: To assess the diabetes empowerment scores and its correlated factors among type 2 diabetes patients in a primary care clinic in Malaysia.

    METHODS: This is a cross sectional study involving 322 patients with type 2 diabetes mellitus (DM) followed up in a primary care clinic. Systematic sampling method was used for patient recruitment. The Diabetes Empowerment Scale (DES) questionnaire was used to measure patient empowerment. It consists of three domains: (1) Managing the psychosocial aspect of diabetes (9 items); (2) Assessing dissatisfaction and readiness to change (9 items); and (3) Setting and achieving diabetes goal (10 items). A score was considered high if it ranged from 100 to 140. Data analysis was performed using SPSS version 25 and multiple linear regressions was used to identify the predictors of total diabetes empowerment scores.

    RESULTS: The median age of the study population was 55 years old. 56% were male and the mean duration of diabetes was 4 years. The total median score of the DES was 110 [interquartile range (IQR) = 10]. The median scores of the three subscales were 40 with (IQR = 4) for "Managing the psychosocial aspect of diabetes"; 36 with (IQR = 3) for "Assessing dissatisfaction and readiness to change"; and 34 with (IQR = 5) for "Setting and achieving diabetes goal". According to multiple linear regressions, factors that had significant correlation with higher empowerment scores among type 2 diabetes patients included an above secondary education level (P < 0.001), diabetes education exposure (P = 0.003), lack of ischemic heart disease (P = 0.017), and lower glycated hemoglobin (HbA1c) levels (P < 0.001).

    CONCLUSION: Diabetes empowerment scores were high among type 2 diabetes patients in this study population. Predictors for high empowerment scores included above secondary education level, diabetes education exposure, lack of ischemic heart disease status and lower HbA1c.

    Matched MeSH terms: Linear Models
  4. Tao H, Rahman MA, Jing W, Li Y, Li J, Al-Saffar A, et al.
    Work, 2021;68(3):903-912.
    PMID: 33720867 DOI: 10.3233/WOR-203424
    BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users.

    OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.

    RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.

    CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.

    Matched MeSH terms: Linear Models
  5. Bong CH, Lau TL, Ab Ghani A, Chan NW
    Water Sci Technol, 2016 Oct;74(8):1876-1884.
    PMID: 27789888
    The understanding of how the sediment deposit thickness influences the incipient motion characteristic is still lacking in the literature. Hence, the current study aims to determine the effect of sediment deposition thickness on the critical velocity for incipient motion. An incipient motion experiment was conducted in a rigid boundary rectangular flume of 0.6 m width with varying sediment deposition thickness. Findings from the experiment revealed that the densimetric Froude number has a logarithmic relationship with both the thickness ratios ts/d and ts/y0 (ts: sediment deposit thickness; d: grain size; y0: normal flow depth). Multiple linear regression analysis was performed using the data from the current study to develop a new critical velocity equation by incorporating thickness ratios into the equation. The new equation can be used to predict critical velocity for incipient motion for both loose and rigid boundary conditions. The new critical velocity equation is an attempt toward unifying the equations for both rigid and loose boundary conditions.
    Matched MeSH terms: Linear Models
  6. Borhani TN, Saniedanesh M, Bagheri M, Lim JS
    Water Res, 2016 07 01;98:344-53.
    PMID: 27124124 DOI: 10.1016/j.watres.2016.04.038
    In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.
    Matched MeSH terms: Linear Models
  7. Gazzaz NM, Yusoff MK, Juahir H, Ramli MF, Aris AZ
    Water Environ Res, 2013 Aug;85(8):751-66.
    PMID: 24003601
    This study investigated relationships of a water quality index (WQI) with multiple water quality variables (WQVs), explored variability in water quality over time and space, and established linear and non-linear models predictive of WQI from raw WQVs. Data were processed using Spearman's rank correlation analysis, multiple linear regression, and artificial neural network modeling. Correlation analysis indicated that from a temporal perspective, the WQI, temperature, and zinc, arsenic, chemical oxygen demand, sodium, and dissolved oxygen concentrations increased, whereas turbidity and suspended solids, total solids, nitrate nitrogen (NO3-N), and biochemical oxygen demand concentrations decreased with year. From a spatial perspective, an increase with distance of the sampling station from the headwater was exhibited by 10 WQVs: magnesium, calcium, dissolved solids, electrical conductivity, temperature, NO3-N, arsenic, chloride, potassium, and sodium. At the same time, the WQI; Escherichia coli bacteria counts; and suspended solids, total solids, and dissolved oxygen concentrations decreased with distance from the headwater. Lastly, regression and artificial neural network models with high prediction powers (81.2% and 91.4%, respectively) were developed and are discussed.
    Matched MeSH terms: Linear Models
  8. Oong XY, Chook JB, Ng KT, Chow WZ, Chan KG, Hanafi NS, et al.
    Virol J, 2018 05 23;15(1):91.
    PMID: 29792212 DOI: 10.1186/s12985-018-1005-8
    BACKGROUND: Human metapneumovirus (HMPV) is established as one of the causative agents of respiratory tract infections. To date, there are limited reports that describe the effect of HMPV genotypes and/or viral load on disease pathogenesis in adults. This study aims to determine the role of HMPV genetic diversity and nasopharyngeal viral load on symptom severity in outpatient adults with acute respiratory tract infections.
    METHODS: Severity of common cold symptoms of patients from a teaching hospital was assessed by a four-category scale and summed to obtain the total symptom severity score (TSSS). Association between the fusion and glycoprotein genes diversity, viral load (quantified using an improved RT-qPCR assay), and symptom severity were analyzed using bivariate and linear regression analyses.
    RESULTS: Among 81/3706 HMPV-positive patients, there were no significant differences in terms of demographics, number of days elapsed between symptom onset and clinic visit, respiratory symptoms manifestation and severity between different HMPV genotypes/sub-lineages. Surprisingly, elderly patients (≥65 years old) had lower severity of symptoms (indicated by TSSS) than young and middle age adults (p = 0.008). Nasopharyngeal viral load did not correlate with nor predict symptom severity of HMPV infection. Interestingly, at 3-5 days after symptom onset, genotype A-infected patients had higher viral load compared to genotype B (4.4 vs. 3.3 log10 RNA copies/μl) (p = 0.003).
    CONCLUSIONS: Overall, HMPV genetic diversity and viral load did not impact symptom severity in adults with acute respiratory tract infections. Differences in viral load dynamics over time between genotypes may have important implications on viral transmission.
    Study site: Primary Care Clinic, University of Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia
    Matched MeSH terms: Linear Models
  9. Lim JM, Hong AG, Raman S, Shyamala N
    Ultrasound Obstet Gynecol, 2000 Feb;15(2):131-7.
    PMID: 10775996
    To determine whether racial differences affect the relationship between the fetal femur diaphysis length and the neonatal crown-heel length.
    Matched MeSH terms: Linear Models
  10. Veerasamy R, Rajak H
    Turk J Pharm Sci, 2021 04 20;18(2):151-156.
    PMID: 33900700 DOI: 10.4274/tjps.galenos.2020.45556
    Objectives: The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for neuraminidase inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anti-influenza activity.

    Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.

    Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.

    Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.

    Matched MeSH terms: Linear Models
  11. Shashvat K, Basu R, Bhondekar PA, Kaur A
    Trop Biomed, 2019 Dec 01;36(4):822-832.
    PMID: 33597454
    Time series modelling and forecasting plays an important role in various domains. The objective of this paper is to construct a simple average ensemble method to forecast the number of cases for infectious diseases like dengue and typhoid and compare it by applying models for forecasting. In this paper we have also evaluated the correlation between the number of typhoid and dengue cases with the ecological variables. The monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. This data was analysed by three models namely support vector regression, neural network and linear regression. The proposed simple average ensemble model was constructed by ensemble of three applied regression models i.e. SVR, NN and LR. We combine the regression models based upon the error metrics such as Mean Square Error, Root Mean Square Error and Mean Absolute Error. It was found that proposed ensemble method performed better in terms of forecast measures. The finding demonstrates that the proposed model outperforms as compared to already available applied models on the basis of forecast accuracy.
    Matched MeSH terms: Linear Models
  12. Thu TV, Loh TC, Foo HL, Yaakub H, Bejo MH
    Trop Anim Health Prod, 2011 Jan;43(1):69-75.
    PMID: 20632092 DOI: 10.1007/s11250-010-9655-6
    A study was carried out to investigate the effects of feeding liquid metabolite combinations produced by Lactobacillus plantarum strains on growth performance, diarrhoea incidence, faecal pH, microfloral counts, short-chain fatty acids (SCFA) and intestinal villus height and crypt depth of postweaning piglets. A total of 120 piglets (26 days old) were randomly assigned evenly into five treatment groups treated with same basal diet: (1) -ve control (free antibiotic); (2) + ve control (0.03% of chlortetracycline); (3) Com 1 (0.3% metabolite of TL1, RG11 and RI11 strains); (4) Com 2 (0.3% metabolite of TL1, RG14 and RS5 strains); (5) Com 3 (0.3% metabolite of RG11, RG14 and RI11 strains). After 5 weeks, the average daily feed intake was not significantly different (P > 0.05) among the treatments and feed conversion ratio was the highest (P 
    Matched MeSH terms: Linear Models
  13. Ho CY, Ibrahim Z, Abu Zaid Z, Mat Daud Z', Md Yusop NB
    Trials, 2020 Jun 16;21(1):533.
    PMID: 32546217 DOI: 10.1186/s13063-020-04462-4
    INTRODUCTION: There has been growing evidence on the favourable outcomes of fast-track-recovery (FTR) surgery; to expedite recovery, minimise complications, and reduce the length of hospital stay for surgical patients. However, there is lack of evidence on the effectiveness of FTR in surgical gynaecological cancer (GC) patients. Most of the previous studies did not focus on feeding composition in the FTR surgery protocol. This study aims to determine the effectiveness of FTR feeding with a whey-protein-infused carbohydrate-loading drink pre-operatively and early oral feeding post-operatively on post-operative outcomes among surgical GC patients.

    METHODS/DESIGN: This open-labelled, randomised controlled trial (RCT) will randomly allocate patients into intervention and control groups. Ambulated Malaysian aged over 18 years and scheduled for elective surgery for (suspected) GC, will be included in this study. The intervention group will be given whey-protein-infused carbohydrate-loading drinks on the evening before their operation and 3 h before their operation as well as started on early oral feeding 4 h post-operatively. The control group will be fasted overnight pre-operation and only allowed plain water, and return to a normal diet is allowed when bowel sounds return post-operatively. The primary outcomes of study are length of post-operative hospital stay, length of clear-fluid tolerance, solid-food tolerance and bowel function. Additional outcome measures are changes in nutritional status, biochemical profile and functional status. Data will be analysed on an intention-to-treat basis.

    TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT03667755. Retrospectively registered on 12 September 2018; Protocol version: version 3 dated 27 September 2017.

    Matched MeSH terms: Linear Models
  14. Skau JK, Nordin AB, Cheah JC, Ali R, Zainal R, Aris T, et al.
    Trials, 2016;17(1):215.
    PMID: 27117703 DOI: 10.1186/s13063-016-1345-x
    Over the past two decades, the population of Malaysia has grown rapidly and the prevalence of diabetes mellitus in Malaysia has dramatically increased, along with the frequency of obesity, hyperlipidaemia and hypertension. Early-life influences play an important role in the development of non-communicable diseases. Indeed, maternal lifestyle and conditions such as gestational diabetes mellitus or obesity can affect the risk of diabetes in the next generation. Lifestyle changes can help to prevent the development of type 2 diabetes mellitus. This is a protocol for an unblinded, community-based, randomised controlled trial in two arms to evaluate the efficacy of a complex behavioural change intervention, combining motivational interviewing provided by a community health promoter and access to a habit formation mobile application, among young Malaysian women and their spouses prior to pregnancy.
    Matched MeSH terms: Linear Models
  15. Nor Hasliza, Mat Desa, Siti Zamira Aida, Mat Jusoh
    MyJurnal
    Agents are the most important marketing tools for company to become a successful in business. Agents not only operate as a channel to customers, but they also play an important role in providing customers with a variety of services before and after the sales. The main purpose of this study is to identify the factor influencing agent’s sales at an Apparel Manufacturing company. There are three categories of agents at the company namely, Trial agent, Basic agent and Premium agent. Based on the sales records in May 2018, the sales of product obtained by Trial Agent is lower than Basic and Premium Agents in this company. Therefore, this study aims to determine difference mean on record sales by agent among three categories of agents. This study also investigates the relationship between sales records by agents and years of experienced in business. Data was collected using questionnaire from 46 active agents at the company. Data was analyzed using One-way Analysis of Variance (ANOVA), Pearson correlation coefficient and Multiple Linear Regression. Result showed that there is a statistically significant difference in the mean sales records among the three of agent’s categories. Furthermore, there is a strong positive correlation between sales records by agent and years of experienced in business. Meanwhile, factors of knowledge and skills in business are most contributed to the agent’s sales. This study can help the company to create a strategic business plan and conducting several workshop trainings for agents to increase their knowledge and skills in business.
    Matched MeSH terms: Linear Models
  16. Asadi-Shekari Z, Moeinaddini M, Sultan Z, Shah MZ, Hamzah A
    Traffic Inj Prev, 2016 08 17;17(6):650-5.
    PMID: 26890058 DOI: 10.1080/15389588.2015.1136739
    OBJECTIVE: A number of efforts have been conducted on travel behavior and transport fatalities at the neighborhood or street level, and they have identified different factors such as roadway characteristics, personal indicators, and design indicators related to transport safety. However, only a limited number of studies have considered the relationship between travel behavior indicators and the number of transport fatalities at the city level. Therefore, this study explores this relationship and how to fill the mentioned gap in current knowledge.

    METHOD: A generalized linear model (GLM) estimates the relationships between different travel mode indicators (e.g., length of motorway per inhabitants, number of motorcycles per inhabitant, percentage of daily trips on foot and by bicycle, percentage of daily trips by public transport) and the number of passenger transport fatalities. Because this city-level model is developed using data sets from different cities all over the world, the impacts of gross domestic product (GDP) are also included in the model.

    CONCLUSIONS: Overall, the results imply that the percentage of daily trips by public transport, the percentage of daily trips on foot and by bicycle, and the GDP per inhabitant have negative relationships with the number of passenger transport fatalities, whereas motorway length and the number of motorcycles have positive relationships with the number of passenger transport fatalities.

    Matched MeSH terms: Linear Models
  17. Harnen S, Umar RS, Wong SV, Wan Hashim WI
    Traffic Inj Prev, 2003 Dec;4(4):363-9.
    PMID: 14630586
    In conjunction with a nationwide motorcycle safety program, the provision of exclusive motorcycle lanes has been implemented to overcome link-motorcycle accidents along trunk roads in Malaysia. However, not much work has been done to address accidents at junctions involving motorcycles. This article presents the development of predictive model for motorcycle accidents at three-legged major-minor priority junctions of urban roads in Malaysia. The generalized linear modeling technique was used to develop the model. The final model reveals that motorcycle accidents are proportional to the power of traffic flow. An increase in nonmotorcycle and motorcycle flows entering the junctions is associated with an increase in motorcycle accidents. Nonmotorcycle flow on major roads had the highest effect on the probability of motorcycle accidents. Approach speed, lane width, number of lanes, shoulder width, and land use were found to be significant in explaining motorcycle accidents at the three-legged major-minor priority junctions. These findings should enable traffic engineers to specifically design appropriate junction treatment criteria for nonexclusive motorcycle lane facilities.
    Matched MeSH terms: Linear Models
  18. Wong HY, Subramaniyan M, Bullen C, Amer Siddiq AN, Danaee M, Yee A
    Tob Induc Dis, 2019;17:65.
    PMID: 31582954 DOI: 10.18332/tid/111355
    INTRODUCTION: The mobile-phone-based Bedfont iCOTM Smokerlyzer® is of unknown validity and reproducibility compared to the widely-used piCO+ Smokerlyzer®. We aimed to compare the validity and reproducibility of the iCOTM Smokerlyzer® with the piCO+ Smokerlyzer® among patients reducing or quitting tobacco smoking.

    METHODS: Methadone-maintained therapy (MMT) users from three centers in Malaysia had their exhaled carbon monoxide (eCO) levels recorded via the piCO+ and iCOTM Smokerlyzers®, their nicotine dependence assessed with the Malay version of the Fagerström Test for Nicotine Dependence (FTND-M), and daily tobacco intake measured via the Opiate Treatment Index (OTI) Tobacco Q-score. Pearson partial correlations were used to compare the eCO results of both devices, as well as the corresponding FTND-M scores.

    RESULTS: Among the 146 participants (mean age 47.9 years, 92.5% male, and 73.3% Malay ethnic group) most (55.5%) were moderate smokers (6-19 cigarettes/day). Mean eCO categories were significantly correlated between both devices (r=0.861, p<0.001), and the first and second readings were significantly correlated for each device (r=0.94 for the piCO+ Smokerlyzer®, p<0.001; r=0.91 for the iCOTM Smokerlyzer®, p<0.001). Exhaled CO correlated positively with FTND-M scores for both devices. The post hoc analysis revealed a significantly lower iCOTM Smokerlyzer® reading of 0.82 (95% CI: 0.69-0.94, p<0.001) compared to that of the piCO+ Smokerlyzer®, and a significant intercept of -0.34 (95% CI: -0.61 - -0.07, p=0.016) on linear regression analysis, suggesting that there may be a calibration error in one or more of the iCOTM Smokerlyzer® devices.

    CONCLUSIONS: The iCOTM Smokerlyzer® readings are highly reproducible compared to those of the piCO+ Smokerlyzer®, but calibration guidelines are required for the mobile-phone-based device. Further research is required to assess interchangeability.

    Matched MeSH terms: Linear Models
  19. Bala AA, Jatau AI, Yunusa I, Mohammed M, Mohammed AH, Isa AM, et al.
    Ther Adv Drug Saf, 2020;11:2042098620935721.
    PMID: 32944213 DOI: 10.1177/2042098620935721
    Introduction: Snakebite envenoming (SBE) is an important occupational and public health hazard especially in sub-Saharan Africa. For optimum management of SBE, adequate knowledge of Snake antivenom (SAV) is very critical among the healthcare practioners in this region. Information related to the knowledge of SAV use in the management of SBE, as well as SAV logistics is scarce among the Health Care Professionals (HCPs) in Nigeria, particularly in the northern region. We therefore aimed to develop, validate and utilize a tool to assess the SAV knowlegde among HCPs in northern Nigeria. We also sought to implement and evaluate an intervention that could improve the SAV knowledge among the HCPs.

    Methods: The proposed study will be conducted in three phases: Phase I will involve the development of the item-pool to be included in the tool, followed by a face, content validity and construct validity. The tool reliability, readability and difficulty index will be determined. Phase II will involve the utilization of the tool to assess baseline SAV knowledge among the HCPs followed by an educational intervention. Multiple Linear Regression analysis will be used to determine the factors associated with SAV knowledge among the HCPs. Lastly, Phase III which will be a repeat of Phase II to assess and evaluate the knowledge after the intervention.

    Discussion: The study design and findings may guide future implementation and streamline the intervention of improving SAV knowledge in HCPs training and practice.

    Lay Summary: Knowledge assessment and educational intervention of snake antivenom among healthcare practitioners in northern Nigeria: a study protocol Snakebite envenoming (SBE) is an important occupational and public health hazard especially in sub-Saharan Africa. For optimum management of SBE, adequate knowledge of snake antivenom (SAV) is very critical among the healthcare practitioners. The baseline knowledge SAV dosage, mode of administration, availability, and logistics is very relevant among healthcare professionals, particularly those that are directly involved in its logistics. It is paramount that SAV is handled and used appropriately. The efforts and advocacy for the availability for more SAV will be in vain if not handled appropriately before they are used. This study protocol aims to develop a tool, to assess SAV knowledge and effects of educational interventions among healthcare professionals (HCPs) in northern Nigeria. This protocol suggests conducting studies in three phases: (a) Development and validation of SAV knowledge assessment tool, (b) Baseline assessment of SAV knowledge assessment tool among HCPs, and (c) Development, implementation and evaluation of an educational intervention to improve SAV knowledge among HCPs in northern Nigeria.

    Matched MeSH terms: Linear Models
  20. Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I
    Theor Biol Med Model, 2013 Sep 18;10:57.
    PMID: 24044669 DOI: 10.1186/1742-4682-10-57
    OBJECTIVE: The classification of Acute Coronary Syndrome (ACS), using artificial intelligence (AI), has recently drawn the attention of the medical researchers. Using this approach, patients with myocardial infarction can be differentiated from those with unstable angina. The present study aims to develop an integrated model, based on the feature selection and classification, for the automatic classification of ACS.

    METHODS: A dataset containing medical records of 809 patients suspected to suffer from ACS was used. For each subject, 266 clinical factors were collected. At first, a feature selection was performed based on interviews with 20 cardiologists; thereby 40 seminal features for classifying ACS were selected. Next, a feature selection algorithm was also applied to detect a subset of the features with the best classification accuracy. As a result, the feature numbers considerably reduced to only seven. Lastly, based on the seven selected features, eight various common pattern recognition tools for classification of ACS were used.

    RESULTS: The performance of the aforementioned classifiers was compared based on their accuracy computed from their confusion matrices. Among these methods, the multi-layer perceptron showed the best performance with the 83.2% accuracy.

    CONCLUSION: The results reveal that an integrated AI-based feature selection and classification approach is an effective method for the early and accurate classification of ACS and ultimately a timely diagnosis and treatment of this disease.

    Matched MeSH terms: Linear Models
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