Establishment and evaluation of Pakistan's trauma registry: Insights from a public sector trauma institute

Background: The need for a trustworthy Trauma Registry (TR) to enhance patient care and direct trauma prevention strategies has been recognized for a very long time. Objectives: This research sought to establish, develop, and assess Pakistan's first Digital TR at SMBB Institute of Trauma. Methods: Using U.S. DIv5 TR model as starting point, locally adapted digital TR was developed, and several methodologies were used to analyze prevalence, characters, and first-aid care of trauma, as well as the feasibility of establishing a national TR. The research was conducted at SMBBIT for the period from November 2016 to December 2022. Results: Majority of patients suffering trauma were youthful 46% and predominantly male 86%, according to the demographic analysis. Direct admission from the accident scene was common 68%, and the preponderance of incidents involved 59% road accidents. In the majority of cases, 64% family members provided primary care. The leading causes of injury were 75% blunt force trauma and 59% automobile collisions. Orthopedic 35%, neurosurgical 22%, and oro-maxillofacial 12% injuries were the most common. In terms of assault-related injuries, gunshot wounds were a prominent cause of trauma.&nbsp

Malay Validation of Copenhagen Psychosocial Work Environment Questionnaire in Context of Second Generation Statistical Techniques

Psychosocial hazards present in workplaces are being actively investigated by researchers from multiple domains. More research and resources are required to investigate the debilitating consequences of these hazards in the developing and underdeveloped countries where this issue remains one of grave concern. This study aims at investigating the psychometric properties of Malaysian version of Copenhagen Psychosocial Questionnaire for reliability and validity purpose. The Malaysian version of COPSOQ is a multidimensional questionnaire; it comprises of 7 major formative constructs and 28 variables with an additional inclusion of two variables which are organizational loyalty and physiological health biomarkers (blood pressure and body mass index) that explicate a reflective construct which has 93 items all catering to assess psychosocial determinants present in workplace environments. Each formative second-order construct is further categorized into different reflective first-order constructs. The focus of this study was only on first-order reflective constructs. Probability sampling was used for data collection from 300 respondents working in industries with a response rate of 100%; structural equation modeling technique was applied for data analysis. All psychometric analysis performed on reflective constructs gave reliable results which demonstrate the validity of Bahasa Melayu (BM-COPSOQ) and its comprehensiveness of including relevant dimensions particularly in context to Asian region. The BM-COPSOQ will fill up the knowledge gap and provide a bridge between researchers, work professionals and practitioners, and many other workplaces for the best understanding of psychosocial work environment

Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression

In precision agriculture, data-intelligent algorithms applied for predicting wheat yield can generate crucial information about enhancing crop production and strategic decision-making. In this chapter, artificial neural network (ANN) model is trained with three neighboring station-based wheat yields to predict the yield for two nearby objective stations that share a common geographic boundary in the agricultural belt of Pakistan. A total of 2700 ANN models (with a combination of hidden neurons, training algorithm, and hidden transfer/output functions) are developed by trial-and-error method, attaining the lowest mean square error, in which the 90 best-ranked models for 3-layered neuronal network are utilized for wheat prediction. Models such as learning algorithms comprised of pure linear, tangent, and logarithmic sigmoid equations in hidden transfer/output functions, executed by Levenberg–Marquardt, scaled conjugate gradient, conjugate gradient with Powell-Beale restarts, Broyden–Fletcher–Goldfarb–Shanno quasi-Newton, Fletcher-Reeves update, one-step secant, conjugate gradient with Polak-Ribiére updates, gradient descent with adaptive learning, gradient descent with momentum, and gradient descent with momentum adaptive learning method are trained. For the predicted wheat yield at objective station 1 (i.e., Toba Taik Singh), the optimal architecture was 3-14-1 (input-hidden-output neurons) trained with the Levenberg–Marquardt algorithm and logarithmic sigmoid as activation and tangent sigmoid as output function, while at objective station 2 (i.e., Bakkar), the Levenberg–Marquardt algorithm provided the best architecture (3-20-1) with pure liner as activation and tangent sigmoid as output function. The results are benchmarked with those from minimax probability machine regression (MPMR) and genetic programming (GP) in accordance with statistical analysis of predicted yield based on correlations (r), Willmott's index (WI), Nash-Sutcliffe coefficient (EV), root mean-squared error (RMSE), and mean absolute error (MAE). For objective station 1, the ANN model attained the r value of approximately 0.983, with WI ≈ 0.984 and EV ≈ 0.962, while the MPMR model attained r ≈ 0.957, WI ≈ 0.544, and EV ≈ 0.527, with the results attained by GP model, r ≈ 0.982, WI ≈ 0.980, and EV ≈ 0.955. For optimal ANN model, a relatively low value of RMSE ≈ 192.02 kg/ha and MAE ≈ 162.75 kg/ha was registered compared with the MPMR (RMSE ≈ 614.46 kg/ha; MAE ≈ 431.29 kg/ha) and GP model (RMSE ≈ 209.25 kg/ha; MAE ≈ 182.84 kg/ha). For both objective stations, ANN was found to be superior, as confirmed by a larger Legates-McCabe's (LM) index used in conjunction with relative RMSE and MAE. Accordingly, it is averred that ANN is considered as a useful data-intelligent contrivance for predicting wheat yield by using nearest neighbor yield