Outcomes following 25-gauge vitrectomies.

PURPOSE: Twenty-five gauge vitrectomy surgery offers potential advantages over standard 20-gauge vitrectomy surgery, but the short- and long-term post-operative complications, such as cataract formation, are still being evaluated. This study quantifies the outcomes seen following 25-gauge vitrectomies. METHODS: This is a retrospective, consecutive, non-comparative case series of 25-gauge vitrectomies performed between January 2002 and August 2004. Cases without at least 3 months of follow-up and previous vitrectomies were excluded. Analyses were performed with t-test and Kaplan-Meier curves. RESULTS: Seventy-one cases met inclusion criteria. The mean age of the patients was 65 years old (SD 11 years). A variety of surgical indications were included. A statistically significant difference was seen between the mean preoperative visual acuity (20/100) and the mean visual acuity at the 3-month post-operative visit (20/60; P<0.0001). A Kaplan-Meier curve established that for all cases 63.4% of eyes required cataract extraction at 1 year. Total mean follow-up time was 8.6+/-5.5 months. CONCLUSIONS: Statistically significant improvement was seen in mean vision by 3 months following 25-gauge vitrectomy. Cataract formation after 25-gauge vitrectomies remains an important consideration.

Test-retest reliability of the ulnar F-wave minimum latency versus ulnar distal motor latency in healthy adults.

BACKGROUND AND PURPOSE: The purposes of this study were to explore reliability of the ulnar F-wave minimum latency (Fmin) and the ulnar distal motor latency (DML) and to contrast those levels of reliability in order to reveal whether physiologic lability is the primary contributor to unwanted variability in Fmin measurements. SUBJECTS AND METHODS: Fmin and DML in the Abductor Digiti Minimi muscle were measured bilaterally by two raters in 50 healthy adults (n = 100 hands, 70 male, 30 female) with 3-14 days between testing sessions. RESULTS: Intrarater reliability (ICC 3,1) for the Fmin was 0.89 with a standard error of the measurement (SEM) of 0.77 msec. Interrater reliability (ICC 2,1) for the Fmin was 0.80 with a SEM of 1.04 msec. Intrarater reliability (ICC 3,1) for the DML was 0.71 with a SEM of 0.18 msec. Interrater reliability (ICC 2,1) for the DML was 0.76 with a SEM of 0.19 msec. DISCUSSION AND CONCLUSIONS: Contrary to our hypothesis, the Fmin had a higher reliability than the DML. The DML did not display the high reliability other investigators have reported. We conclude the Fmin is a reliable measurement when 10 supramaximal stimulations are administered to healthy, young to middle-aged adult subjects. However, no inferences were made regarding relative levels of psychologic lability for the two latencies.

The benefit of stereology for quantitative radiology.

A knowledge of stereology (i.e. proper sampling), the opportunities provided by computers for image analysis (i.e. image segmentation, image registration, data base exploration, 3D reconstruction), and the strengths (i.e. non-invasive) and limitations (i.e. finite resolution, image artefacts) of medical imaging equipment must all be combined for reliable quantitative magnetic resonance imaging (MRI), the goal of which is to obtain a deeper understanding of the structure, function, life cycle and evolution of the human body, especially the brain, and a more objective diagnosis of disease and assessment of its response to treatment. In this article we illustrate the first of these requirements. We describe the application of proper sampling strategies and efficient computer-based counting procedures for obtaining unbiased estimates of volume by the Cavalieri method and of surface area from vertical sections. In particular, we estimate the volume of a brain tumour from Cavalieri sections, the volume of grey matter in the cerebral hemispheres from Cavalieri slices and the surface area of the cerebral cortex from vertical sections. The estimates obtained are mathematically unbiased. In each case, we assess the precision of the estimates empirically. Application of formulae available for predicting the precision of volume estimates obtained using the Cavalieri sections and slices methods is also described.

Estimation of brain compartment volume from MR Cavalieri slices.

PURPOSE: Recent theory has been developed to estimate volume from a systematic sample of tissue slices of a given thickness and to predict the corresponding error. Our goal was to check the error prediction formulas by resampling and to determine the minimum number of MR slices required to estimate the volumes of the cerebrum and of the compartments of gray matter (GM) and white matter (WM) with prescribed errors. METHOD: Our working data set comprised the GM and WM segmentations obtained from a paradigmatic high signal-to-noise ratio 3D spoiled GRASS MR volume data set for a single healthy human subject. The data were classified using a fuzzy clustering minimum distance algorithm. We thereby obtained a stack of 183 serial coronal slices of 1 mm thickness encompassing the whole cerebrum. Empirical resampling was carried out using the corresponding data vectors, and the theoretical error predictors were thereby checked for slice thicknesses of 1, 3, 9, and 27 mm, with a distance of 45 mm between slice midplanes. RESULTS: Irrespective of slice thickness, a minimum of 3, 5, and 10 slices provided estimates of the true total volume of GM and WM in the cerebrum with coefficients of error (CEs) of 10, 5, and 3%, respectively, where CE(V)% = 100 x SE(V)/V. For the cerebrum, a minimum of two, three, and four slices were required for CEs of the same precision. CONCLUSION: In combination with high signal-to-noise ratio and enhanced tissue contrast, Cavalieri slices are the most appropriate for MRI, they supply unbiased and highly efficient volume estimates of brain compartments. For a given number of slices, CE(V) decreases rapidly when the slices are thicker than the gaps between them; when the slices are thinner than the gaps, then CE(V) is similar to that in the situation when the slice thickness is zero.