Pharmaceutical e-commerce: evidence concerning the quality of medicines available from the internet

The overall aim is to appraise and extend the evidence base on poor quality medicines found in online medicine quality surveys. The thesis starts with two systematic reviews critically appraising the existing evidence, finding a range of evidence gaps subsequently addressed in the following chapters. These include: (a) inconsistent and poor quality reporting, (b) the methods employed are subject to high degrees of risk of bias, (c) discrepancies exist in scientific evidence, and (d) a range of critical therapeutic areas require investigation of the incidence of poor quality medicines. The Delphi consensus study of Chapter 5 constructs the first reporting guidelines to standardise reporting, allowing for evidence-based conclusions. Chapter 6 finds four out of nine investigated withdrawn medicines still available for purchase in a range of countries worldwide (clobenzorex, fenfluramine, rimonabant, and sibutramine), which are subsequently acquired and chemically analysed in Chapter 7. The results from Chapter 8 find a mean of 7.7% poor quality medicines, which corroborates with the findings of the systematic review of Chapter 4, finding a mean of 10% (IQR 0-20%) poor quality medicines in online medicine quality surveys. Purchased medicines displayed a range of quality defects, including orders lacking patient information leaflets, failing pharmacopoeia bioavailability testing, the absence of various types of packaging, the non-delivery of paid orders, and failure of pharmacopoeia friability testing.</p

Pharmaceutical e-commerce: evidence concerning the quality of medicines available from the internet

The overall aim is to appraise and extend the evidence base on poor quality medicines found in online medicine quality surveys. The thesis starts with two systematic reviews critically appraising the existing evidence, finding a range of evidence gaps subsequently addressed in the following chapters. These include: (a) inconsistent and poor quality reporting, (b) the methods employed are subject to high degrees of risk of bias, (c) discrepancies exist in scientific evidence, and (d) a range of critical therapeutic areas require investigation of the incidence of poor quality medicines. The Delphi consensus study of Chapter 5 constructs the first reporting guidelines to standardise reporting, allowing for evidence-based conclusions. Chapter 6 finds four out of nine investigated withdrawn medicines still available for purchase in a range of countries worldwide (clobenzorex, fenfluramine, rimonabant, and sibutramine), which are subsequently acquired and chemically analysed in Chapter 7. The results from Chapter 8 find a mean of 7.7% poor quality medicines, which corroborates with the findings of the systematic review of Chapter 4, finding a mean of 10% (IQR 0-20%) poor quality medicines in online medicine quality surveys. Purchased medicines displayed a range of quality defects, including orders lacking patient information leaflets, failing pharmacopoeia bioavailability testing, the absence of various types of packaging, the non-delivery of paid orders, and failure of pharmacopoeia friability testing.</p

Transgender health content in medical education: a theory-guided systematic review of current training practices and implementation barriers & facilitators

Health disparities faced by transgender people are partly explained by barriers to trans-inclusive healthcare, which in turn are linked to a lack of transgender health education in medical school curricula. We carried out a theory-driven systematic review with the aim to (1) provide an overview of key characteristics of training initiatives and pedagogical features, and (2) analyze barriers and facilitators to implementing this training in medical education. We used queer theory to contextualize our findings. We searched the PubMed/Ovid MEDLINE database (October 2009 to December 2021) for original studies that reported on transgender content within medical schools and residency programs (N = 46). We performed a thematic analysis to identify training characteristics, pedagogical features, barriers and facilitators. Most training consisted of single-session interventions, with varying modes of delivery. Most interventions were facilitated by instructors with a range of professional experience and half covered general LGBT+-content. Thematic analysis highlighted barriers including lack of educational materials, lack of faculty expertise, time/costs constraints, and challenges in recruiting and compensating transgender guest speakers. Facilitators included scaffolding learning throughout the curriculum, drawing on expertise of transgender people and engaging learners in skills-based training. Sustainable implementation of transgender-health objectives in medical education faces persistent institutional barriers. These barriers are rooted in normative biases inherent to biomedical knowledge production, and an understanding of categories of sex and gender as uncomplicated. Medical schools should facilitate trans-inclusive educational strategies to combat transgender-health inequities, which should include a critical stance toward binary conceptualizations of sex and gender throughout the curriculum

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

OBJECTIVES: Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors. METHODS: We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards. RESULTS: In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6–100% and 66.7–100% and specificities ranged from 76.3–100%; 69–100% and 77.8–100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set. CONCLUSION: Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable