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Data pre-processing for the preterm prediction study MFMU dataset
Preterm birth is a major public health problem with profound implications on society. There would be extreme value in being able to identify women at risk of preterm birth during the course of their pregnancy. Previous research has largely focused on individual risk factors correlated with preterm birth (e.g. prior preterm birth, race, and infection) and less on combining these factors in a way to understand the complex etiologies of preterm birth. We attempt to address this gap by conducting a deeper analysis of the preterm prediction study data collected by the NICHD Maternal Fetal Medicine Units (MFMU) Network, a high-quality data for over 3,000 singleton pregnancies having detailed study visits and biospecimen collection at 24, 26, 28 and 30 weeks gestation. Reports from this dataset used relatively straightforward biostatitistical methodologies such as relative risk assessments to measure associations between risk factors and PTB (Maternal Fetal Medicine Units Net- work. Biostatistical Coordinating Center NICHD Networks, 1995). These methods include descriptive statistics, Pearson correlation, Fisher’s exact tests and linear/logistic regression where risk factors are studied independent of each other. In order to perform detailed experiments on this data using non-linear Support Vector Machines and other machine learning (ML) methodologies, it is necessary to complete several pre-processing steps that we describe in this report
The educational value of ward rounds as a learning and teaching opportunity for house officers, medical officers, and registrars in Sudanese hospitals: a multi-center cross-sectional study
Abstract Background Ward rounds are a cornerstone in the educational experience of junior doctors and an essential part of teaching patient care. Here, we aimed to assess the doctors’ perception of ward rounds as an educational opportunity and to identify the obstacles faced in conducting a proper ward round in Sudanese hospitals. Method A cross-sectional study was conducted from the 15th to the 30th of January 2022 among house officers, medical officers, and registrars in about 50 teaching and referral hospitals in Sudan. House officers and medical officers were considered the learners, while specialist registrars were considered the teachers. Doctors’ perceptions were assessed using an online questionnaire, with a 5-level Likert scale to answer questions. Results A total of 2,011 doctors participated in this study (882 house officers, 697 medical officers, and 432 registrars). The participants were aged 26.9 ± 3.2 years, and females constituted about 60% of the sample. An average of 3.1 ± 6.8 ward rounds were conducted per week in our hospitals, with 11.1 ± 20.3 h spent on ward rounds per week. Most doctors agreed that ward rounds are suitable for teaching patient management (91.3%) and diagnostic investigations (89.1%). Almost all the doctors agreed that being interested in teaching (95.1%) and communicating appropriately with the patients (94.7%) make a good teacher in ward rounds. Furthermore, nearly all the doctors agreed that being interested in learning (94.3%) and communicating appropriately with the teacher (94.5%) make a good student on ward rounds. About 92.8% of the doctors stated that the quality of ward rounds could be improved. The most frequently reported obstacles faced during ward rounds were the noise (70%) and lack of privacy (77%) in the ward environment. Conclusion Ward rounds have a special value in teaching patient diagnosis and management. Being interested in teaching/learning and having good communication skills were the two major criteria that make a good teacher/learner. Unfortunately, ward rounds are faced with obstacles related to the ward environment. It is mandatory to ensure the quality of both ward rounds' teaching and environment to optimize the educational value and subsequently improve patient care practice