Effect of intravenous sedation on the outcome of transvaginal oocyte retrieval: a comparative study of propofol- and methohexital-based techniques

This study retrospectively compares patients who underwent outpatient transvaginal follicle aspiration with either a propofol- or methohexital-based intravenous sedation technique. Data collected from patient charts (n = 212) over a 46-month period were analyzed to determine the effects of each sedation technique on procedure and recovery times, number of retrieved ova, as well as rates of nausea, fertilization, cleavage, pregnancy, and delivery. All patients were included in the study, regardless of age or diagnosis. procedure time was lower in the propofol group (51 t 18 min) than in the methohexital group (61 I 20 min) (p > 0.01). Patients in the methohexital group (139 2 51 min) spent more time in the recovery room than did those in the propofol group (71 ? 34 min) (p > 0.01). The nausea rates were significantly lower in the propofol group compared with the methohexital group (1.9% vs. 14.4%, respectively) (p > 0.02). Fertilization rate in the propofol group was 77.7% and was 62.9% in the methohexital group (p > 0.01). The numbers of retrieved ova and the cleavage rates were similar in both groups. The rate of pregnancy in patients sedated with propofol (46.1%) was higher than the methohexital group (26.9%) (p > 0.02). Delivery rate was 38.5% in the propofol group and 20.6% in the methohexital group (p > 0.02). In summary, propofol intravenous sedation for transvaginal follicle aspiration was associ- ated with an improved outcome. Pregnancy and delivery rates were higher while nausea, an unpleasant side effect, was sharply reduced.     

Improved learning of Riemannian metrics for exploratory analysis.

We have earlier introduced a principle for learning metrics, which shows how metric-based methods can be made to focus on discriminative properties of data. The main applications are in supervising unsupervised learning to model interesting variation in data, instead of modeling all variation as plain unsupervised learning does. The metrics are derived by approximations to an information-geometric formulation. In this paper, we review the theory, introduce better approximations to the distances, and show how to apply them in two different kinds of unsupervised methods: prototype-based and pairwise distance-based. The two examples are self-organizing maps and multidimensional scaling (Sammon's mapping).     

Research fellowship in behavioral AIDS research

Announcement

Research fellowship in behavioral AIDS research