Subjective logic as a complementary tool to meta-analysis to explicitly address second-order uncertainty in research findings: A case from infant studies.

Any experiment brings about results and conclusions that necessarily have a component of uncertainty. Many factors influence the degree of this uncertainty, yet they can be overlooked when drawing conclusions from a body of research. Here, we showcase how subjective logic could be employed as a complementary tool to meta-analysis to incorporate the chosen sources of uncertainty into the answer that researchers seek to provide to their research question. We illustrate this approach by focusing on a body of research already meta-analyzed, whose overall aim was to assess if human infants prefer prosocial agents over antisocial agents. We show how each finding can be encoded as a subjective opinion, and how findings can be aggregated to produce an answer that explicitly incorporates uncertainty. We argue that a core feature and strength of this approach is its transparency in the process of factoring in uncertainty and reasoning about research findings. Subjective logic promises to be a powerful complementary tool to incorporate uncertainty explicitly and transparently in the evaluation of research.

Do maternal haemodynamics have a causal influence on treatment for gestational diabetes?

BACKGROUND: Arterial stiffening is believed to contribute to the worsening of insulin resistance, and factors which are associated with needing pharmacological treatment of gestational diabetes (GDM), such as maternal obesity or advanced age, are associated with impaired cardiovascular adaptation to pregnancy. In this observational study, we aimed to investigate causal relationships between maternal haemodynamics and treatment requirement amongst women with GDM. METHODS: We assessed maternal haemodynamics in women with GDM, comparing those who remained on dietary treatment with those who required pharmacological management. Maternal haemodynamics were assessed using the Arteriograph® (TensioMed Ltd, Budapest, Hungary) and the NICOM® non-invasive bio-reactance method (Cheetah Medical, Portland, Oregon, USA). A graphical causal inference technique was used for statistical analysis. RESULTS: 120 women with GDM were included in the analysis. Maternal booking BMI was identified as having a causative influence on treatment requirement, with each unit increase in BMI increasing the odds of needing metformin and/or insulin therapy by 12% [OR 1.12 (1.02 - 1.22)]. The raw values of maternal heart rate (87.6 ± 11.7 vs. 92.9 ± 11.90 bpm, p = 0.014) and PWV (7.8 ± 1.04 vs. 8.4 ± 1.61 m/s, p = 0.029) were both significantly higher amongst the women requiring pharmacological management, though these relationships did not remain significant in causal logistic regression. CONCLUSIONS: Maternal BMI at booking has a causal, rather than simply associational, relationship on the need for pharmacological treatment of GDM. No significant causal relationships were found between maternal haemodynamics and the need for pharmacological treatment.

This observational study is the first to examine relationships between maternal haemodynamics and treatment requirement for gestational diabetes (GDM). This is also the first study to demonstrate a causative, rather than simply associational, relationship between maternal body mass index (BMI) and the need for pharmacological treatment of GDM, with each unit increase in BMI increasing the odds of needing metformin and/or insulin therapy by 12%. Maternal heart rate and pulse wave velocity were significantly higher among women with GDM requiring pharmacological management, but this finding did not remain significant in logistic regression analysis, and no causative relationships between maternal hemodynamics and treatment requirement were identified. Our findings highlight the importance of pre- and peri-conception weight control, but do not support a role for measurement of maternal hemodynamics in the prediction of women who are likely to require pharmacological management of GDM.

Predicting Postpartum Hemorrhage (PPH) during Cesarean Delivery Using the Leicester PPH Predict Tool: A Retrospective Cohort Study.

OBJECTIVE: The aim of the present study was to develop a toolkit combining various risk factors to predict the risk of developing a postpartum hemorrhage (PPH) during a cesarean delivery. STUDY DESIGN: A retrospective cohort study of 24,230 women who had cesarean delivery between January 2003 and December 2013 at a tertiary care teaching hospital within the United Kingdom serving a multiethnic population. Data were extracted from hospital databases, and risk factors for PPH were identified. Hothorn et al recursive partitioning algorithm was used to infer a conditional decision tree. For each of the identified combinations of risk factors, two probabilities were calculated: the probability of a patient producing ≥1,000 and ≥ 2,000 mL blood loss. RESULTS: The Leicester PPH predict score was then tested on the randomly selected remaining 25% (n = 6,095) of the data for internal validity. Reliability testing showed an intraclass correlation of 0.98 and mean absolute error of 239.8 mL with the actual outcome. CONCLUSION: The proposed toolkit enables clinicians to predict the risk of postpartum hemorrhage. As a result, preventative measures for postpartum hemorrhage could be undertaken. Further external validation of the current toolkit is required.

Validation and discovery from computational biology models.

Simulation software is often a fundamental component in systems biology projects and provides a key aspect of the integration of experimental and analytical techniques in the search for greater understanding and prediction of biology at the systems level. It is important that the modelling and analysis software is reliable and that techniques exist for automating the analysis of the vast amounts of data which such simulation environments generate. A rigorous approach to the development of complex modelling software is needed. Such a framework is presented here together with techniques for the automated analysis of such models and a process for the automatic discovery of biological phenomena from large simulation data sets. Illustrations are taken from a major systems biology research project involving the in vitro investigation, modelling and simulation of epithelial tissue.