Category: Nevin Manimala

Malays J Med Sci. 2023 Apr;30(2):153-160. doi: 10.21315/mjms2023.30.2.14. Epub 2023 Apr 18.

ABSTRACT

BACKGROUND: The vaccinated travel lane (VTL) between Malaysia and Singapore was implemented to facilitate travelling between countries without the need for quarantine.

OBJECTIVES: i) Observe the rate of positive SARS-CoV-2 test results amongst inbound international travellers; ii) Explore possible factors associated with the outcome of test results between VTL and non-VTL travellers.

METHOD: This is a retrospective cross-sectional study involving air travellers arriving in Malaysia via the Kuala Lumpur International Airport (KLIA) or Kuala Lumpur International Airport 2 (KLIA2) who were tested for SARS-CoV-2 by reverse transcriptase polymerase chain reaction (RT-PCR) from 29 November 2021 to 15 March 2022. Subject demographics and RT-PCR results were retrieved from the laboratory information system and statistically analysed.

RESULTS: Out of 118,902 travellers, mostly were Malaysian nationals (62.7%) and VTL travellers (68.2%) with a median age of 35 years old. A total of 699 (0.6%) of travellers tested positive on arrival, out of which 70.2% had cycle threshold (Ct) values > 30 (70.8% of VTL and 70.0% of non-VTL cohorts). Non-VTL travellers were 4.5 times more likely to test positive compared with VTL travellers (1.25% versus 0.28%; P < 0.001).

CONCLUSION: Tighter entry requirements including vaccination status and testing frequency, the use of sensitive detection methods on arrival and similar public health policies between countries may have contributed to the VTL being a safe and cost-effective mode of travel.

PMID:37102055 | PMC:PMC10125236 | DOI:10.21315/mjms2023.30.2.14

J Educ Eval Health Prof. 2023;20:14. doi: 10.3352/jeehp.2023.20.14. Epub 2023 Apr 27.

ABSTRACT

PURPOSE: The aim of this study was to identify factors influencing the learning transfer of nursing students in a non-face-to-face educational environment through structural equation modeling and suggest ways to improve the transfer of learning.

METHODS: In this cross-sectional descriptive study, data were collected via online surveys from February 9 to March 1, 2022, from 218 nursing students in Korea. Learning transfer, learning immersion, learning satisfaction, learning efficacy, self-directed learning ability and information technology utilization ability were analyzed using IBM SPSS for Windows ver. 22.0 and AMOS ver. 22.0.

RESULTS: The assessment of structural equation modeling showed adequate model fit, with normed χ2=1.74 (P<0.024), goodness-of-fit index=0.97, adjusted goodness-of-fit index=0.93, comparative fit index=0.98, root mean square residual=0.02, Tucker-Lewis index=0.97, normed fit index=0.96, and root mean square error of approximation=0.06. In a hypothetical model analysis, 9 out of 11 pathways of the hypothetical structural model for learning transfer in nursing students were statistically significant. Learning self-efficacy and learning immersion of nursing students directly affected learning transfer, and subjective information technology utilization ability, self-directed learning ability, and learning satisfaction were variables with indirect effects. The explanatory power of immersion, satisfaction, and self-efficacy for learning transfer was 44.4%.

CONCLUSION: The assessment of structural equation modeling indicated an acceptable fit. It is necessary to improve the transfer of learning through the development of a self-directed program for learning ability improvement, including the use of information technology in nursing students’ learning environment in non-face-to-face conditions.

PMID:37100591 | DOI:10.3352/jeehp.2023.20.14

Int Forum Allergy Rhinol. 2023 Apr 26. doi: 10.1002/alr.23173. Online ahead of print.

ABSTRACT

BACKGROUND: Nasal airway obstruction (NAO) is a highly prevalent disorder. Septal swell body (SSB) hypertrophy is an often-overlooked contributor to NAO. SSB treatment may relieve symptoms of NAO. The objective of this study was to assess the clinical use of a temperature-controlled radiofrequency (TCRF) device to treat SSBs to improve symptoms in adults with NAO.

METHODS: In this prospective, multicenter, open-label, single arm study, patients with severe or extreme NAO related to SSB hypertrophy received bilateral TCRF treatment in the SSB area. The primary endpoint was improvement in Nasal Obstruction Symptom Evaluation (NOSE) scale scores from baseline to 3 months post-procedure. A subset of study patients underwent computed tomography (CT) imaging to evaluate post-treatment changes in SSB size.

RESULTS: Mean NOSE scale scores significantly improved from 73.5 (SD 14.2) at baseline to 27.9 (SD 17.2) at 3 months post-procedure; a reduction of -45.3 (SD 21.4), (95% confidence interval [CI]: -50.4 to -40.1; p< 0.0001): the responder rate was 95.7% (95% CI: 0.88 to 0.99; p < 0.0001). CT evaluation at 3 months showed statistically significant reductions in the SSB with the greatest reduction in the middle thickness: (mean change -3.4 [SD 1.8] millimeters [95% CI: -4.0 to -2.8; p<.0001]). Minimal adverse events with any relationship to the device or procedure were reported, none were serious in nature and no septal perforations occurred.

CONCLUSIONS: This study demonstrates that temperature-controlled radiofrequency treatment of SSB hypertrophy is well-tolerated and effective at reducing both SSB size and symptoms of NAO at 3 months post-treatment.

PMID:37100587 | DOI:10.1002/alr.23173

Indian J Tuberc. 2023 Apr;70(2):214-221. doi: 10.1016/j.ijtb.2022.05.005. Epub 2022 May 22.

ABSTRACT

BACKGROUND: Quality of life is a significant issue among patients with tuberculosis and is used for evaluating treatment responses and therapeutic outcome. This study aimed to assess the quality of life in tuberculosis patients receiving anti-tuberculosis therapy for a short duration in the Vellore district of Tamil Nadu and its associated variables.

METHODS: A cross-sectional study was designed to evaluate pulmonary tuberculosis patients receiving treatment under category -1 registered in the NIKSHAY portal at Vellore. A total of 165 pulmonary tuberculosis patients were recruited from March 2021 to the third week of June 2021. On obtaining informed consent, the data were collected through the telephone interview by administering WHOQOL- BREF structured questionnaire. The data were examined with descriptive and analytical statistics. Multiple regression analysis for independent quality of life variables was done.

RESULTS: The lowest median scores, 31(25,38) & 38(25,44) was, related to psychological and environmental domains, respectively. In addition, the Man-Whitney & Kruskal Wallis showed a statistically significant variation in the mean quality of life for gender, employment status, duration of treatment, persistent symptoms, the location of residence of patients, and the stage of therapy. Age, gender, marital status, and persistent symptoms were the main associating factor.

CONCLUSION: Tuberculosis and its treatment influence psychological, physical functioning, and the environmental domain of patient quality of life. Attention is required in the follow-up and treatment of patients by monitoring their quality of life.

PMID:37100578 | DOI:10.1016/j.ijtb.2022.05.005

Indian J Tuberc. 2023 Apr;70(2):168-175. doi: 10.1016/j.ijtb.2022.03.028. Epub 2022 Apr 6.

ABSTRACT

BACKGROUND: TB continues to ravage high burden countries despite aggressive TB control measures. Poverty and adverse socioeconomic and cultural factors play a significant role in stigmatization, causing delayed health care seeking, non-compliance to treatment and spread of disease in the community. Women are more vulnerable to stigmatization, posing the risk of gender inequality in health care. The objectives of this study were to ascertain the degree of stigmatization and gender disparity in TB related stigma in the community.

METHODS: Study was conducted among TB unaffected persons, using consecutive sampling from bystanders of patients attending the hospital for diseases other than TB. Closed structured questionnaire was used for measuring socio-demographic, knowledge and stigma variables. Stigma scoring was done using TB vignette.

RESULTS: Majority subjects (119 males and 102 females) were from rural area and low socioeconomic status; more than 60% of males and females having college education. Half the subjects answered more than half the TB knowledge questions correctly. Knowledge score was significantly lower among females compared with males (p < 0.002) despite high literacy. Overall stigma scoring was low (mean score = 15.9; total 75). Stigma was higher among females compared with males (p < 0.002); more profound among females receiving female vignettes (Chi-square = 14.1, p < 0.0001). The association was significant even after adjusting for co-variables (OR = 3.323, P = 0.005). Low knowledge showed minimal (statistically insignificant) association with stigma.

CONCLUSIONS: Perceived stigma though low, was more among females and much higher with female vignette, indicating significant gender disparity in stigma towards TB.

PMID:37100573 | DOI:10.1016/j.ijtb.2022.03.028

Artif Intell Med. 2023 May;139:102542. doi: 10.1016/j.artmed.2023.102542. Epub 2023 Apr 5.

ABSTRACT

BACKGROUND/INTRODUCTION: Manual detection and localization of the brain’s epileptogenic areas using electroencephalogram (EEG) signals is time-intensive and error-prone. An automated detection system is, thus, highly desirable for support in clinical diagnosis. A set of relevant and significant non-linear features plays a major role in developing a reliable, automated focal detection system.

METHODS: A new feature extraction method is designed to classify focal EEG signals using eleven non-linear geometrical attributes derived from the Fourier-Bessel series expansion-based empirical wavelet transform (FBSE-EWT) segmented rhythm’s second-order difference plot (SODP). A total of 132 features (2 channels × 6 rhythms × 11 geometrical attributes) were computed. However, some of the obtained features might be non-significant and redundant features. Hence, to acquire an optimal set of relevant non-linear features, a new hybridization of ‘Kruskal-Wallis statistical test (KWS)’ with ‘VlseKriterijuska Optimizacija I Komoromisno Resenje’ termed as the KWS-VIKOR approach was adopted. The KWS-VIKOR has a two-fold operational feature. First, the significant features are selected using the KWS test with a p-value lesser than 0.05. Next, the multi-attribute decision-making (MADM) based VIKOR method ranks the selected features. Several classification methods further validate the efficacy of the features of the selected top n%.

RESULTS: The proposed framework has been evaluated using the Bern-Barcelona dataset. The highest classification accuracy of 98.7% was achieved using the top 35% ranked features in classifying the focal and non-focal EEG signals with the least-squares support vector machine (LS-SVM) classifier.

CONCLUSIONS: The achieved results exceeded those reported through other methods. Hence, the proposed framework will more effectively assist the clinician in localizing the epileptogenic areas.

PMID:37100511 | DOI:10.1016/j.artmed.2023.102542

Artif Intell Med. 2023 May;139:102539. doi: 10.1016/j.artmed.2023.102539. Epub 2023 Mar 31.

ABSTRACT

Certain life-threatening abnormalities, such as cholangiocarcinoma, in the human biliary tract are curable if detected at an early stage, and ultrasonography has been proven to be an effective tool for identifying them. However, the diagnosis often requires a second opinion from experienced radiologists, who are usually overwhelmed by many cases. Therefore, we propose a deep convolutional neural network model, named biliary tract network (BiTNet), developed to solve problems in the current screening system and to avoid overconfidence issues of traditional deep convolutional neural networks. Additionally, we present an ultrasound image dataset for the human biliary tract and demonstrate two artificial intelligence (AI) applications: auto-prescreening and assisting tools. The proposed model is the first AI model to automatically screen and diagnose upper-abdominal abnormalities from ultrasound images in real-world healthcare scenarios. Our experiments suggest that prediction probability has an impact on both applications, and our modifications to EfficientNet solve the overconfidence problem, thereby improving the performance of both applications and of healthcare professionals. The proposed BiTNet can reduce the workload of radiologists by 35% while keeping the false negatives to as low as 1 out of every 455 images. Our experiments involving 11 healthcare professionals with four different levels of experience reveal that BiTNet improves the diagnostic performance of participants of all levels. The mean accuracy and precision of the participants with BiTNet as an assisting tool (0.74 and 0.61, respectively) are statistically higher than those of participants without the assisting tool (0.50 and 0.46, respectively (p<0.001)). These experimental results demonstrate the high potential of BiTNet for use in clinical settings.

PMID:37100509 | DOI:10.1016/j.artmed.2023.102539

Artif Intell Med. 2023 May;139:102536. doi: 10.1016/j.artmed.2023.102536. Epub 2023 Mar 29.

ABSTRACT

OBJECTIVE: Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs.

METHODS: Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly.

RESULTS: Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes.

CONCLUSION: There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.

PMID:37100507 | DOI:10.1016/j.artmed.2023.102536

Artif Intell Med. 2023 May;139:102534. doi: 10.1016/j.artmed.2023.102534. Epub 2023 Mar 28.

ABSTRACT

BACKGROUND: Indigenous peoples often have higher rates of morbidity and mortality associated with cardiometabolic disease (CMD) than non-Indigenous people and this may be even more so in urban areas. The use of electronic health records and expansion of computing power has led to mainstream use of artificial intelligence (AI) to predict the onset of disease in primary health care (PHC) settings. However, it is unknown if AI and in particular machine learning is used for risk prediction of CMD in Indigenous peoples.

METHODS: We searched peer-reviewed literature using terms associated with AI machine learning, PHC, CMD, and Indigenous peoples.

RESULTS: We identified 13 suitable studies for inclusion in this review. Median total number of participants was 19,270 (range 911-2,994,837). The most common algorithms used in machine learning in this setting were support vector machine, random forest, and decision tree learning. Twelve studies used the area under the receiver operating characteristic curve (AUC) to measure performance. Two studies reported an AUC of >0.9. Six studies had an AUC score between 0.9 and 0.8, 4 studies had an AUC score between 0.8 and 0.7. 1 study reported an AUC score between 0.7 and 0.6. Risk of bias was observed in 10 (77 %) studies.

CONCLUSION: AI machine learning and risk prediction models show moderate to excellent discriminatory ability over traditional statistical models in predicting CMD. This technology could help address the needs of urban Indigenous peoples by predicting CMD early and more rapidly than conventional methods.

PMID:37100506 | DOI:10.1016/j.artmed.2023.102534

Tob Control. 2023 Apr 26:tc-2023-057931. doi: 10.1136/tc-2023-057931. Online ahead of print.

ABSTRACT

BACKGROUND: In 2020, Ethiopia passed a landmark tax proclamation implementing an evidence-based mixed excise system aimed at curbing tobacco use. This study evaluates the impact of the tax increase of more than 600% on both legal and illegal cigarette prices in order to gauge the impact of the tax reform in the presence of a sizeable illicit cigarette market.

METHODS: Data on 1774 cigarette prices were obtained from retailers during Empty Cigarette Pack Surveys in the capital and major regional cities conducted in 2018 and 2022. Packs were categorised as ‘legal’ or ‘illicit’ using criteria from the tobacco control directives. Descriptive and regression analyses were used to study the cigarette price changes during the period of 2018-2022, capturing the impact of the 2020 tax increase.

RESULT: Prices of both legal and illegal cigarettes increased in response to the tax increase. In 2018, the stick prices ranged from ETB0.88 (Ethiopian birr) to ETB5.00 for legal cigarettes while they ranged from ETB0.75 to ETB3.25 for illegal ones. In 2022, a legal stick sold for ETB01.50-ETB2.73 and an illegal stick for ETB1.92-ETB8.00. The average real price of legal and illegal brands increased by 18% and 37%, respectively. The multivariate analysis confirms that prices of illicit cigarettes grew faster compared with the legal ones. By 2022, illicit brands were on average more expensive compared with their legal counterparts. This result is statistically significant at p<0.01.

CONCLUSION: The prices of both legal and illegal cigarettes increased following the 2020 tax increase, with the average real cigarette price increasing by 24%. As a result, the tax increase likely had a positive impact on public health despite a sizeable illicit cigarette market.

PMID:37100452 | DOI:10.1136/tc-2023-057931