Long-term effects of supervised exercise training on walking capacity and quality of life in patients with intermittent claudication

Supervised exercise training has been shown to improve walking capacity in several studies of patients with intermittent claudication. However, data on long-term outcome are quite limited. The aim of this prospective study was to evaluate long-term effects of supervised exercise training on walking capacity and quality of life in patients with intermittent claudication. Patients and methods: Sixty-seven consecutive patients with intermittent claudication who completed a supervised 12-week exercise training program were asked for follow up evaluation 39 +/- 20 months after program completion. Pain-free walking distance (PWD) and maximum walking distances (MWD) were assessed by treadmill test and several questionnaires. Results: Forty (60%) patients agreed to participate, 22 (33%) refused participation, and 5 (7%) died during follow-up. PWD and MWD significantly improved at completion of 12-weeks supervised exercise training as compared to baseline (PWD 114 +/- 100 vs. 235 +/- 248, p = 0.002; MWD 297 +/- 273 vs. 474 +/- 359, p = 0.001). Improvement of PWD and MWD could be maintained at follow up (197 +/- 254, p = 0.014; 390 +/- 324, p = 0.035, respectively) with non-smokers showing significantly better sustained PWD and MWD improvement as compared to baseline. Overall, walking capacity correlated with functional status of quality of life. Conclusions: Major findings of this investigation were that improvement in walking capacity is sustained after completion of supervised exercise training program with best results in patients who quitted or never smoked. Improved walking capacity is associated with increased functional status of quality of life.

Effect of 6-month supervised exercise on low-density lipoprotein apolipoprotein B kinetics in patients with type 2 diabetes mellitus

Although low-density lipoprotein (LDL) cholesterol is often normal in patients with type 2 diabetes mellitus, there is evidence for a reduced fractional catabolic rate and consequently an increased mean residence time (MRT), which can increase atherogenic risk. The dyslipidemia and insulin resistance of type 2 diabetes mellitus can be improved by aerobic exercise, but effects on LDL kinetics are unknown. The effect of 6-month supervised exercise on LDL apolipoprotein B kinetics was studied in a group of 17 patients with type 2 diabetes mellitus (mean age, 56.8 years; range, 38-68 years). Patients were randomized into a supervised group, who had a weekly training session, and an unsupervised group. LDL kinetics were measured with an infusion of 1-(13)C leucine at baseline in all groups and after 6 months of exercise in the patients. Eight body mass index-matched nondiabetic controls (mean age, 50.3 years; range, 40-67 years) were also studied at baseline only. At baseline, LDL MRT was significantly longer in the diabetic patients, whereas LDL production rate and fractional clearance rates were significantly lower than in controls. Percentage of glycated hemoglobin A(1c), body mass index, insulin sensitivity measured by the homeostasis model assessment, and very low-density lipoprotein triglyceride decreased (P < .02) in the supervised group, with no change in the unsupervised group. After 6 months, LDL cholesterol did not change in either the supervised or unsupervised group; but there was a significant change in LDL MRT between groups (P < .05) that correlated positively with very low-density lipoprotein triglyceride (r = 0.51, P < .04) and negatively with maximal oxygen uptake, a measure of fitness (r = -0.51, P = .035), in all patients. The LDL production and clearance rates did not change in either group. This study suggests that a supervised exercise program can reduce deleterious changes in LDL MRT.

Impact of surgical training on incidence of surgical site infection

BACKGROUND: Despite availability of other training forms, tutorial assistance cannot be entirely replaced in surgical education. Concerns exist that tutorial assistance may lead to an increased rate of surgical site infection (SSI). The purpose of the present study was to investigate whether the risk of SSI is higher after surgery with tutorial assistance than after surgery performed autonomously by a fully trained surgeon. METHODS: All consecutive visceral, vascular, and traumatological inpatient procedures at a Swiss University Hospital were prospectively recorded during a 24-month period, and the patients were followed for 12 months to ascertain the occurrence of SSI. Using univariable and multivariable logistic regressions, we assessed the association of tutorial assistance surgery with SSI in 6,103 interventions. RESULTS: Autonomously performed surgery was associated with SSI in univariable analysis (5.36% SSI vs. 3.81% for tutorial assistance, p = 0.006). In multivariable analysis, the odds of SSI for tutorial assistance was no longer significantly lower (Odds Ratio [OR] = 0.82; 95% Confidence Interval [CI]: 0.62-1.09; p = 0.163). CONCLUSIONS: Surgical training does not lead to higher SSI rate if trainees are adequately supervised and interventions are carefully selected. Although other forms of training are useful, tutorial assistance in the operating room continues to be the mainstay of surgical education.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.