Rosales Hooper (pyjamawhite5)

Post-fistula-repair incontinence (PFRI) is a common complication of vesicovaginal fistula (VVF) surgeries. It entails continuous leakage of urine after successful VVF closure. Pelvic Floor Muscle Training (PFMT) plays a vital role in the management of PFRI, however, an evolving exercise approach is the Paula Exercise Method (PEM) which has shown a promising effect in stopping urinary incontinence, but there is no data on its effect on PFRI. This study therefore, proposes to primarily investigate the effect of PEM on urine leakage and secondarily, pelvic floor strength (PFS), quality of life (QoL), sexual function (SF), and mental health (MH) in women with PFRI. This is a study protocol for a randomized controlled trial. A total of 182 participants are expected to participate in the study after satisfying the inclusion criteria. The participants will be randomized into either PEM or PFMT study groups. The demographic data of all the participants will be recorded. Each participant will be assessed for urine leakage, PFS, QoL, SF, and MH at baseline and subsequently, at four, eight and 12weeks of intervention. Demographic parameters will be summarized using descriptive statistics. Continuous data will be computed for differences using inferential statistic of Analysis of variance, t-test and Man Whitney U as appropriate. All analyses will be performed using SPSS version 22.0 with probability set at 0.05 alpha level. It is hoped that the outcome of this study will determine the effect of the Paula exercise method on urine leakage, pelvic floor strength, quality of life, sexual function, and mental health among women with post-fistula-repair incontinence and also provide evidence for the use of the Paula method in urinary incontinence. Pan African Clinical Trials Registry ( ), identifier PACTR201906515532827. Pan African Clinical Trials Registry ( ), identifier PACTR201906515532827. In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance. EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models. Gasless trans-axillary endoscopic thyroidectomy (GTAET) has satisfactory cosmetic effects for the patients who have benign goiter and small thyroid carcinoma, however the complications of this surgical procedure have not been fully documented. Ipsilateral hypoglossal nerve palsy (IHNP) associated with GTAET has never been reported before. A 33-year old male patient presented with a 4 × 5mm solid thyroid nodule in the right lobe. Papillary thyroid carcinoma was confirmed by the fine needle aspiration. He had strong cosmetic demand, therefore GTAET for right lobectomy and central cervical lymphadenectomy