Evaluating the effectiveness of text messaging and phone call reminders to minimize no show at pediatric outpatient clinics in Pakistan: protocol for a mixed-methods study

Background: Missing health care appointments without canceling in advance results in a no show, a vacant appointment slot that cannot be offered to others. No show can be reduced by reminding patients about their appointment in advance. In this regard, mobile health (mHealth) strategy is to use text messaging (short message service, SMS), which is available on all cellular phones, including cheap low-end handsets. Nonattendance for appointments in health care results in wasted resources and disturbs the planned work schedules.Objectives: The purpose of this study is to evaluate the efficacy of the current text messaging (SMS) and call-based reminder system and further explore how to improve the attendance at the pediatric outpatient clinics. The primary objectives are to (1) determine the efficacy of the current clinic appointment reminder service at pediatric outpatient clinics at Aga Khan University Hospital, (2) assess the mobile phone access and usage among caregivers visiting pediatrics consultant clinics, and (3) explore the perception and barriers of parents regarding the current clinic appointment reminder service at the pediatric outpatient clinics at Aga Khan University Hospital.Methods: The study uses a mixed-method design that consists of 3 components: (1) retrospective study (component A) which aims to determine the efficacy of text messaging (SMS) and phone call–based reminder service on patient’s clinic attendance during January to June 2017 (N=58,517); (2) quantitative (component B) in which a baseline survey will be conducted to assess the mobile phone access and usage among parents/caregivers of children visiting pediatrics consultant clinics (n=300); and (3) qualitative (component C) includes in-depth interviews and focus group discussion with parents/caregivers of children visiting the pediatric consultancy clinic and with health care providers and administrative staff. Main constructs will be to explore perceptions and barriers related to existing clinic appointment reminder service. Ethics approval has been obtained from the Ethical Review Committee, Aga Khan University, Pakistan (4770-Ped-ERC-17).Results: Results will be disseminated to pediatric quality public health and mHealth communities through scientific meetings and through publications, nationally and internationally.Conclusions: This study will provide insight regarding efficacy of using mHealth-based reminder services for patient’s appointments in low- and middle-income countries setup. The finding of this study will be used to recommend further enhanced mHealth-based solutions to improve patient appointments and decrease no show

Role of multidetector computed tomography (MDCT) in patients with ovarian masses

Objective:To evaluate the diagnostic accuracy of multidetector 64-slice computed tomography (MDCT) in the diagnosis and differentiation of benign and malignant ovarian masses using histopathology and surgical findings as the gold standard. Material And Methods: This study was conducted in Aga Khan University Hospital, Karachi, Pakistan. Data was reviewed retrospectively from 1 November 2008 to 12 December 2009. One hundred Patients found to have ovarian masses on CT scan were included in the study. CT scan was performed in all these Patients after administration of oral and IV contrast. Ovarian masses were classified as benign and malignant on scan findings. Imaging findings were compared with histopathologic results and surgical findings. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of MDCT were calculated. Results: MDCT was found to have 97% sensitivity, 91% specificity, and an accuracy of 96% in the differentiation of benign and malignant ovarian masses, while PPV and NPV were 97% and 91%, respectively. Conclusion: MDCT imaging offers a safe, accurate and noninvasive modality to differentiate between benign and malignant ovarian masses

Discordant interpretation of serial bone mineral density measurements by dual-energy X-ray absorptiometry using vendor\u27s and institutional least significant changes: Serious impact on decision-making

Meaningful change in bone mineral density (BMD) should be equal or higher than institutional least significant change (LSC). But some facilities use vendor\u27s LSC which is discouraged by International Society for Clinical Densitometry (ISCD). The aim of this study was to find the impact of scan interpretation upon interval BMD changes using vendors and institutional LSCs. This prospective study was conducted at Joint Commission International-accredited facility of Pakistan from April-June 2017 using Hologic Discovery-A scanner. As per ISCD recommendations, precision error and LSC of two technologists were measured. Serial BMD changes such as deterioration or improvement interpreted based on vendor\u27s and institutional LSCs were compared. Serial BMD changes in 102 patients were included, having a mean age, male:female ratio, and mean body mass index of 63 years, 94%:06%, and 29.274 kg/m2, respectively. Mean menopausal age was 47 years and mean duration between two dual X-ray absorptiometry (DXA) studies was 3 years. BMD changes over hip were found significant in 55% and 53% cases against vendor\u27s and institutional LSCs, respectively (nonsignificant discordance in 2%). BMD changes using vendor\u27s and institutional LSCs were found significant over L1-4 (62% vs. 46%; discordance: 14%) and distal forearm (77% vs. 35%; discordance: 41%), respectively. Interpretations based on vendor\u27s LSCs revealed significantly overestimated deterioration over forearm and improvement over L1-4 BMD values. We conclude that vendor\u27s provided LSC for interpretation of serial DXA is misleading and has a significant negative impact upon patients\u27 management. Every DXA facility must use its own LSC as per ISCD guidelines. Furthermore, ISCD must consider publishing cutoff values for LSC for distal forearm measurement

Association of Stress, Knowledge Management, and Change with Organizational Effectiveness in Education Sector of Pakistan

The purpose of this study is to analyze the impact of organizational stress, knowledge management, and organizational change on organizational effectiveness. A valid questionnaire was distributed to administrative staff and faculty members of different educational institutes. 100 questionnaires were distributed in public and private educational sectors. 75 complete questionnaires were received at response rate of 75%. A non probability random sampling technique was used to select the sample. Pearson’s moment correlation and linear regression was applied to study the relationship between organizational stress, knowledge management, organizational change and organizational effectiveness. Results show significant relationship of factors and positive impact on organizational effectiveness. This research also discusses practical implicatios and research limitations.&nbsp

Improving news headline text generation quality through frequent POS-tag patterns analysis

Original synthetic content writing is one of the human abilities that algorithms aspire to emulate. The advent of sophisticated algorithms, especially based on neural networks has shown promising results in recent times. A watershed moment was witnessed when the attention mechanism was introduced which paved the way for transformers, a new exciting architecture in natural language processing. Recent sensations like GPT and BERT for synthetic text generation rely on NLP transformers. Although, GPT and BERT-based models are capable of generating creative text given they are properly trained on abundant data, however, the generated text suffers the quality aspect when limited data is available. This is especially an issue for low-resource languages where labeled data is still scarce. In such cases, the generated text, more often than not, lacks the proper sentence structure, thus unreadable. This study proposes a post-processing step in text generation that improves the quality of generated text through the GPT model. The proposed post-processing step is based on the analysis of POS tagging patterns in the original text and accepts only those generated sentences from GPT which satisfy POS patterns that are originally learned from the data. We exploit the GPT model to generate English headlines by utilizing Australian Broadcasting Corporation (ABC) news dataset. Furthermore, for assessing the applicability of the model in low-resource languages, we also train the model on the Urdu news dataset for Urdu news headlines generation. The experiments presented in this paper on these datasets from high- and low-resource languages show that the performance of generated headlines has a significant improvement by using the proposed headline POS pattern extraction. We evaluate the performance through subjective evaluation as well as using text generation quality metrics like BLEU and ROUGE

Prediction of Pakistani Honey Authenticity through Machine Learning

Honey is a high-demand product in many countries because it is high in nutritional value and rich in antioxidants. Thus, the demand for honey is increased. However, the productivity of honey is naturally lower than its demand. Therefore, honey has often become a target for adulteration. Adulteration of honey is a critical issue because the nutritional value of pure honey is reduced by adding cheap and easily available sweeteners, affecting the consumers’ health. Thus, investigating honey authenticity is popular among regulatory bodies, the food industry, retail sellers, and consumers. Several works have been done to predict the authenticity of honey using various physicochemical features. Few other works have also classified honey on the basis of geographical or botanical origin. However, previous studies have three major limitations. First, the existing studies used the imbalanced datasets, and the performance of these studies further needs attention. Second, as far as we know, no researcher has attempted to use machine learning approaches in investigating the adulteration of Pakistani honey. Finally, the dataset for predicting the authenticity of Pakistani honey is lacking. Therefore, this study proposes a novel classification model to address the aforementioned weaknesses by classifying the authenticity of Pakistani honey using machine learning algorithms and several physicochemical features. This work also presents three classification models systematically to classify the Pakistani honey into three levels. The first level classifies whether the honey is original or branded. The second level classifies the geographical origin. The botanical origin of honey is classified in the third level. Our experimental results show that the proposed features coupled with machine learning algorithms can predict the authenticity of Pakistani honey with outstanding results. We believe that our proposed work will be proved beneficial in reducing the adulteration of Pakistani honey

Precision Agriculture using Internet of thing with Artificial intelligence: A Systematic Literature Review

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this revie

Green HRM practices and corporate sustainability performance

Purpose: The existing literature indicates that the ultimate purpose of green human resource management (GHRM) practices is to enhance sustainable corporate performance by shaping employees’ green behaviors. In this vein, we argue that green organizational culture and employee existing pro-environmental behaviors are the important factors or channels through which GHRM practices shape green employee behaviors for sustainable corporate performance. Consequently, we draw on the ability, motivation, and opportunity (AMO) framework to examine how firms’ GHRM practices indirectly shape employee green behavior for sustainable corporate performance by cultivating and reinforcing green organizational culture under the boundary condition of high employee pro-environmental behavior. Design/methodology/approach: This study uses multi-source, dyadic, and time-lagged data collected from green HR managers and employees in 242 ISO-14001-certified green firms in the Kingdom of Saudi Arabia. The study applies structural equation modeling through LISREL 12 software for testing of hypotheses. Findings: The findings support the postulation that GHRM practices, directly and indirectly, shape employee green behaviors for sustainable performance. GHRM practices indirectly enhance employee green behaviors for sustainable performance by cultivating and fostering the green organizational culture in the presence of high pro-environmental behavior. Practical implications: This study outlines theoretical and practical implications on how HRM managers require an established green organizational culture and employee pro-environmental behaviors to effectively direct GHRM for enhanced sustainable corporate performance. HRM managers should make use of appropriate interventions, including but not limited to GHRM practices, to foster a green organizational culture and employee pro-environmental behaviors. Originality/value: This is an original study that outlines the importance of alignment between Green HRM practices and employee pro-environmental behaviors towards shaping green organizational culture and employee behaviors for corporate sustainability. The study demonstrates how GHRM practices enhance sustainable corporate performance through sequential mediations of green organizational culture and employee green behaviors, and under the boundary condition of pro-environmental behavior

Effects of a high-dose 24-h infusion of tranexamic acid on death and thromboembolic events in patients with acute gastrointestinal bleeding (HALT-IT): an international randomised, double-blind, placebo-controlled trial

Background: Tranexamic acid reduces surgical bleeding and reduces death due to bleeding in patients with trauma. Meta-analyses of small trials show that tranexamic acid might decrease deaths from gastrointestinal bleeding. We aimed to assess the effects of tranexamic acid in patients with gastrointestinal bleeding. Methods: We did an international, multicentre, randomised, placebo-controlled trial in 164 hospitals in 15 countries. Patients were enrolled if the responsible clinician was uncertain whether to use tranexamic acid, were aged above the minimum age considered an adult in their country (either aged 16 years and older or aged 18 years and older), and had significant (defined as at risk of bleeding to death) upper or lower gastrointestinal bleeding. Patients were randomly assigned by selection of a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients received either a loading dose of 1 g tranexamic acid, which was added to 100 mL infusion bag of 0·9% sodium chloride and infused by slow intravenous injection over 10 min, followed by a maintenance dose of 3 g tranexamic acid added to 1 L of any isotonic intravenous solution and infused at 125 mg/h for 24 h, or placebo (sodium chloride 0·9%). Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was death due to bleeding within 5 days of randomisation; analysis excluded patients who received neither dose of the allocated treatment and those for whom outcome data on death were unavailable. This trial was registered with Current Controlled Trials, ISRCTN11225767, and ClinicalTrials.gov, NCT01658124. Findings: Between July 4, 2013, and June 21, 2019, we randomly allocated 12 009 patients to receive tranexamic acid (5994, 49·9%) or matching placebo (6015, 50·1%), of whom 11 952 (99·5%) received the first dose of the allocated treatment. Death due to bleeding within 5 days of randomisation occurred in 222 (4%) of 5956 patients in the tranexamic acid group and in 226 (4%) of 5981 patients in the placebo group (risk ratio [RR] 0·99, 95% CI 0·82–1·18). Arterial thromboembolic events (myocardial infarction or stroke) were similar in the tranexamic acid group and placebo group (42 [0·7%] of 5952 vs 46 [0·8%] of 5977; 0·92; 0·60 to 1·39). Venous thromboembolic events (deep vein thrombosis or pulmonary embolism) were higher in tranexamic acid group than in the placebo group (48 [0·8%] of 5952 vs 26 [0·4%] of 5977; RR 1·85; 95% CI 1·15 to 2·98). Interpretation: We found that tranexamic acid did not reduce death from gastrointestinal bleeding. On the basis of our results, tranexamic acid should not be used for the treatment of gastrointestinal bleeding outside the context of a randomised trial

Production of green and renewable biodiesel from marine brown alga <i>Sargassum tenerrimum</i>

815-821The present research work demonstrates the production of biodiesel from the oily content of marine macroalga Sargassum tenerrimum via mechanical stirring and microwave irradiations. The algal oil as well as direct algal biomas were subjected to methanolysis and ethanolysis using Na metal, NaOH and H2SO4 as catalysts. Mechanical stirring was found to be relatively slow but more feasible method for transesterification while microwave irradiation was observed to be too fast method with certain limitations like vigorous bumping that would be uncontrolable on large scale. Na metal was the most reactive catalyst that produced FAME (82%) and FAEE (80%) by mechanical stirring at room temperature whereas it produced FAME (88%) and FAEE (85%) by microwave heating within 1-5 minutes. Na metal was found to be very reactive, NaOH was the moderate while H2SO4 was the slowest catalyst for transesterification. Methanol was found to be more reactive due to its smaller size as compared to ethanol. Algal oil produced significant amount of biodiesel as compared to the algal biomass due to maximum interaction of reactants with oil. Biodiesel production was confirmed by TLC examination and by comparing the fuel properties of biodiesel with the ASTM standard limits of biodiesel