METHODS: Retrospective data of 347 patients, consisting of maternal demographics and ultrasound parameters collected between the 20th and 25th gestational weeks, were studied. ML models were applied to different combinations of the parameters to predict SGA and severe SGA at birth (defined as 10th and third centile birth weight).
RESULTS: Using second-trimester measurements, ML models achieved an accuracy of 70% and 73% in predicting SGA and severe SGA whereas clinical guidelines had accuracies of 64% and 48%. Uterine PI (Ut PI) was found to be an important predictor, corroborating with existing literature, but surprisingly, so was nuchal fold thickness (NF). Logistic regression showed that Ut PI and NF were significant predictors and statistical comparisons showed that these parameters were significantly different in disease. Further, including NF was found to improve ML model performance, and vice versa.
CONCLUSION: ML could potentially improve the prediction of SGA at birth from second-trimester measurements, and demonstrated reduced NF to be an important predictor. Early prediction of SGA allows closer clinical monitoring, which provides an opportunity to discover any underlying diseases associated with SGA.
METHODS: A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches.
RESULTS: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation.
CONCLUSION: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.
Methods: This cross-sectional study used the sequential exploratory type of mixed methods design in which quantitative data analysis was performed via survey-based questionnaires and qualitative study. For this purpose, we performed a thematic analysis of semi-structured interview questions that were administered to all participants using the self-interview technique.
Results: A majority of students were of the opinion that the process of making poster preparation acted as an opportunity to promote deep learning. Moreover, a majority expressed that making these presentations required teamwork, which gave them an insight into collaborative learning.
Conclusion: Our study revealed that poster presentations, when used effectively as an assignment, can facilitate a learner's critical and reflective thinking and promoting active learning. Previous generic guidelines for making posters were found to be an important step that led to a systematic scientific approach amongst learners as well as for integrating basic science and medical knowledge.
METHODS: A crossover study was conducted among Year 1 and Year 2 pharmacy students. Students were invited to participate voluntarily for one OB and one CB online formative test in a chemistry module in each year. Evaluation of their learning approach and perception of the OB and CB systems of examination was conducted using Deep Information Processing (DIP) questionnaire and Student Perception questionnaire respectively. The mean performance scores of OB and CB examinations were compared.
RESULTS: Analysis of DIP scores showed that there was no significant difference (p > 0.05) in the learning approach adopted for the two different examination systems. However, the mean score obtained in the OB examination was significantly higher (p < 0.01) than those obtained in the CB examination. Preference was given by a majority of students for the OB examination, possibly because it was associated with lower anxiety levels, less requirement of memorization, and more problem solving.
CONCLUSION: There is no difference in deep learning approach of students, whether the format is of the OB or CB type examinations. However, the performance of students was significantly better in OB examination than CB. Hence, using OB examination along with CB examination will be useful for student learning and help them adapt to growing and changing knowledge in pharmacy education and practice.