10 research outputs found
Earthquake Preparedness in Schools of Islamabad
Background: Pakistan is located in the Himalayan region, which has the highest vulnerability to earthquakes. The Islamabad-Rawalpindi area lies in a tectonically active zone, where earthquakes have been frequent in the recent geological past. Earthquake preparedness in schools is relatively under studied area in Pakistan. The objective of this study was to determine the level of preparedness of schools for earthquakes and to compare it between urban and rural, government and private schools in Islamabad Capital Territory.
Material and Methods: It was a cross-sectional comparative study. The study population was schools of Islamabad Capital Territory. The total sample size was 74 and non-probability purposive sampling technique was used. Data was collected through a structured questionnaire pretested on 5% of the sample size and respondents were administrative staff of schools. Data was analyzed using SPSS version 21. Data of the categorical variables was presented in the form of frequencies (percentages). Statistical significance of association between school profile and level of preparedness was determined by using bivariate tabular association analysis (Chi square).
Results: Out of 74 schools, 61 were private and 14 were government schools. 46 schools were located in urban areas while 28 were in rural area of Islamabad Capital Territory. Out of the total schools, 66.2% had written preparedness plan for earthquake and 73% of the schools had a safety committee to lead disaster response planning. Most of the schools (82.4%) had minimum of two exits in high occupancy rooms. There was significant association of location of school with preparedness plan for earthquake (p=0.009), and type of school with two evacuation drills annually (p=0.03).
Conclusion: Private schools and those located in urban areas are better prepared for earthquakes as compared to government schools and the ones located in rural areas
Career Preferences and its influencing factors among medical graduates and undergraduates
Objective: To determine the career preferences of medical students and young doctors and the associated influencing factors.
Methodology: A descriptive cross-sectional study was conducted on 267 graduates and undergraduates of various medical institutes and hospitals of Islamabad., selected through convenient non-probability sampling technique. Data was collected through a self-designed pre-tested questionnaire and processed in SPSS software version 20. Comparison was done among male and female students as well as graduates and undergraduates. For categorical variables frequency and percentages were calculated. χ² test was used to find association between influencing factors, career choices and socio demographic variables.
Results: There was significant difference of specialty preferences between males and females (p= 0.017) as the most preferred specialty in males was General Medicine (23.8%) whereas in females it was Gynaecology (21.7%). Graduates and undergraduates also had significant difference in their specialty preference (p=0.008). The students showed overall equally little interest in the subjects such as Anaesthesia, Oncology, Family Medicine, Public Health and Research. Lack of specialists in a particular field was the most common reason for preference of specialty among males. Females were more influenced by advice from family members in pursuing a career as compared to males (p=0.04). Maximum participants (95.5%) thought that there is a need of career counselling in medical field.
Conclusion: It was observed that majority prefer to choose the most established disciplines. Various factors influence the specialty choices of medical students which should be kept in mind to avoid mismatching of the personality with selection of choice
Effect of early tranexamic acid administration on mortality, hysterectomy, and other morbidities in women with post-partum haemorrhage (WOMAN): an international, randomised, double-blind, placebo-controlled trial
Background
Post-partum haemorrhage is the leading cause of maternal death worldwide. Early administration of tranexamic acid reduces deaths due to bleeding in trauma patients. We aimed to assess the effects of early administration of tranexamic acid on death, hysterectomy, and other relevant outcomes in women with post-partum haemorrhage.
Methods
In this randomised, double-blind, placebo-controlled trial, we recruited women aged 16 years and older with a clinical diagnosis of post-partum haemorrhage after a vaginal birth or caesarean section from 193 hospitals in 21 countries. We randomly assigned women to receive either 1 g intravenous tranexamic acid or matching placebo in addition to usual care. If bleeding continued after 30 min, or stopped and restarted within 24 h of the first dose, a second dose of 1 g of tranexamic acid or placebo could be given. Patients were assigned by selection of a numbered treatment pack from a box containing eight numbered packs that were identical apart from the pack number. Participants, care givers, and those assessing outcomes were masked to allocation. We originally planned to enrol 15 000 women with a composite primary endpoint of death from all-causes or hysterectomy within 42 days of giving birth. However, during the trial it became apparent that the decision to conduct a hysterectomy was often made at the same time as randomisation. Although tranexamic acid could influence the risk of death in these cases, it could not affect the risk of hysterectomy. We therefore increased the sample size from 15 000 to 20 000 women in order to estimate the effect of tranexamic acid on the risk of death from post-partum haemorrhage. All analyses were done on an intention-to-treat basis. This trial is registered with ISRCTN76912190 (Dec 8, 2008); ClinicalTrials.gov, number NCT00872469; and PACTR201007000192283.
Findings
Between March, 2010, and April, 2016, 20 060 women were enrolled and randomly assigned to receive tranexamic acid (n=10 051) or placebo (n=10 009), of whom 10 036 and 9985, respectively, were included in the analysis. Death due to bleeding was significantly reduced in women given tranexamic acid (155 [1·5%] of 10 036 patients vs 191 [1·9%] of 9985 in the placebo group, risk ratio [RR] 0·81, 95% CI 0·65–1·00; p=0·045), especially in women given treatment within 3 h of giving birth (89 [1·2%] in the tranexamic acid group vs 127 [1·7%] in the placebo group, RR 0·69, 95% CI 0·52–0·91; p=0·008). All other causes of death did not differ significantly by group. Hysterectomy was not reduced with tranexamic acid (358 [3·6%] patients in the tranexamic acid group vs 351 [3·5%] in the placebo group, RR 1·02, 95% CI 0·88–1·07; p=0·84). The composite primary endpoint of death from all causes or hysterectomy was not reduced with tranexamic acid (534 [5·3%] deaths or hysterectomies in the tranexamic acid group vs 546 [5·5%] in the placebo group, RR 0·97, 95% CI 0·87-1·09; p=0·65). Adverse events (including thromboembolic events) did not differ significantly in the tranexamic acid versus placebo group.
Interpretation
Tranexamic acid reduces death due to bleeding in women with post-partum haemorrhage with no adverse effects. When used as a treatment for postpartum haemorrhage, tranexamic acid should be given as soon as possible after bleeding onset.
Funding
London School of Hygiene & Tropical Medicine, Pfizer, UK Department of Health, Wellcome Trust, and Bill & Melinda Gates Foundation
Career Preferences and its influencing factors among medical graduates and undergraduates
Objective: To determine the career preferences of medical students and young doctors and the associated influencing factors.
Methodology: A descriptive cross-sectional study was conducted on 267 graduates and undergraduates of various medical institutes and hospitals of Islamabad., selected through convenient non-probability sampling technique. Data was collected through a self-designed pre-tested questionnaire and processed in SPSS software version 20. Comparison was done among male and female students as well as graduates and undergraduates. For categorical variables frequency and percentages were calculated. χ² test was used to find association between influencing factors, career choices and socio demographic variables.
Results: There was significant difference of specialty preferences between males and females (p= 0.017) as the most preferred specialty in males was General Medicine (23.8%) whereas in females it was Gynaecology (21.7%). Graduates and undergraduates also had significant difference in their specialty preference (p=0.008). The students showed overall equally little interest in the subjects such as Anaesthesia, Oncology, Family Medicine, Public Health and Research. Lack of specialists in a particular field was the most common reason for preference of specialty among males. Females were more influenced by advice from family members in pursuing a career as compared to males (p=0.04). Maximum participants (95.5%) thought that there is a need of career counselling in medical field.
Conclusion: It was observed that majority prefer to choose the most established disciplines. Various factors influence the specialty choices of medical students which should be kept in mind to avoid mismatching of the personality with selection of choice
The knowledge regarding Breast Cancer, its risk factors, and screening practices among women from Islamabad, Pakistan
Introduction: Breast Cancer is the rising Public health problem of the world. Pakistan is bearing a high disease burden not only in Asian countries but in the whole world. Pakistan ranks highest in Breast cancer and accounts for almost 34.6%of female cancers. The incidence of the disease in Asian countries is quite different from that in Western countries regarding age i.e. (40-50 years.) while (60-70 years) in Western countries. This study was based on assessing the knowledge regarding breast cancer, risk factors, and screening practices to determine the barriers in the path of the community to seek medical care.Materials and Methods: A cross-sectional study was done between October to December 2019 on 310 females participants of ages from 25 to 70 years, residents of Islamabad, the capital of Pakistan, and knowledge was assessed by applying a self-responding questionnaire.Results: Using SPSS version 23 and chi-square tests, the results showed that 87.7% of participants knew about the prevalence of Breast Cancer. Whereas, 90.3% of the females with the disease are not aware of their illness and show a strong association (p-value is less than 0.05) between knowledge regarding breast cancer and screening tests. Conclusion: The study concluded that 90% of socio-cultural barriers are in the path of access to medical facilities and 90% of participants believed that the non-availability of female doctors in health facilities is a big barrier to access to health. Access to medical facilities should be made easy by promoting health education and removing the fear of results, making small health facility units.
 
Prediction of deep myometrial infiltration, clinical risk category, histological type, and lymphovascular space invasion in women with endometrial cancer based on clinical and T2-weighted MRI radiomic features
Deep myometrial infiltration, clinical risk score, histological type, and lymphovascular space invasion are important clinical variables that have significant management implications for endometrial cancer patients. Determination of these factors using pure T2-weighted MRI is time-consuming, and the accuracy of this relies on the experience of the clinicians. Combining clinical information and radiomic features from MRI, we developed machine learning classification models to predict these clinical variables. Based on a training dataset, an automatic selection classification model with an optimized hyperparameters method was adopted to find the optimal classifiers. The accuracy of the model predictions was evaluated using an independent external testing dataset. The results suggest that an integrated model (combining clinical and radiomic features) achieved a reasonable accuracy for endometrial cancer clinical variable prediction. The application of these models in clinical practice could potentially lead to cost reductions and personalized treatment
Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features
Abstract Background Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis. Methods Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. Results Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. Conclusions The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. Trial registration ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017
Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features
Purpose: To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images. Methods: A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models. Results: Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively. Conclusion: It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods