Shortening the Early Treatment Diabetic Retinopathy Study visual acuity test utilizing a novel computer software: reproducibility in control and patient eyes.

Purpose: To describe and compare a method of computerized visual acuity (VA) testing software to the Early Treatment Diabetic Retinopathy Study (ETDRS) chart.

Methods: Setting: Single tertiary institution.

Study Population: Prospective study including right eyes of volunteers (N = 109) and patients (N = 126).

Ovarian size as a risk factor for recurrent adnexal torsion: Smaller is not better.

Aim: To investigate characteristics and risk factors for recurrent adnexal torsion (AT).

Methods: Retrospective cohort study in a university-affiliated medical center included 320 Women with AT verified by laparoscopy, from January 2005 through January 2017. Demographic data, clinical symptoms, surgical findings and treatment were retrospectively reviewed from patient records.

Does stress affect IVF outcomes? A prospective study of physiological and psychological stress in women undergoing IVF.

Research Question: What are the effects of physiological and psychological stress on fertility outcomes for women undergoing IVF?

Design: A prospective cohort study of 72 patients undergoing IVF in 2017 and 2018. Physiological stress was assessed by salivary cortisol measurements: (i) pretreatment, when the patient received the IVF protocol; (ii) before oocyte retrieval (follicular cortisol was also measured); and (iii) before embryo transfer. Emotional stress was evaluated at each assessment with the State-Trait Anxiety Inventory and a 1-10 Visual Analogue Scale (VAS, referred to as the 'Stress Scale'.

A Comparison of different scoring terminations rules for visual acuity testing: from a computer simulation to a clinical study.

: To compare four visual acuity (VA) scoring termination rules. : A computer simulation generated 30,000 virtual patients who underwent 10 repetitions for each of four termination rules, on both the Snellen and ETDRS charts (2.4 million tests performed in total).

Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests.

We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes.