Intracapsular coblation tonsillectomy versus extracapsular coblation tonsillectomy: a systematic review and a meta-analysis.

PURPOSE: To assess all available data regarding the comparative benefit of intracapsular coblation tonsillectomy (ICT) versus extracapsular coblation tonsillectomy (ECT) in children. METHODS: MEDLINE, the Cochrane Library and Springerlink databases as well as other sources were searched by two independent reviewers. Controlled studies comparing ICT versus ECT in paediatric patients with obstructive sleep apnea (OSA) or recurrent tonsillitis were included. Overall postoperative pain was the primary outcome. Secondary outcomes were postoperative hemorrhage, diet and activity, duration of operation and tonsillar regrowth. In case of homogenous, processable data (I2 < 60%), a meta-analysis was performed. RESULTS: Six studies met the inclusion criteria. The analysis showed significant difference between the two methods in terms of late postoperative pain with the ICT being less painful (SMD - 0.78, 95% CI [- 1.03, - 0.53]). However, there was no significant difference in early postoperative pain (≤ 48 h) between the two techniques (SMD - 0.18, 95% CI [- 0.47, 0.12]). All the rest of the secondary outcomes are presented in a qualitative synthesis due to published data limitations of the included studies. CONCLUSION: Intracapsular coblation tonsillectomy appears to be a less painful operation in comparison to extracapsular coblation tonsillectomy. This seems to occur due to prevention of late pain flare up that normally happens several days after the procedure (described as postoperative dip) and not due to reduced pain at the immediate postoperative period. However, as all studies published are small sized, high-quality, large-sample studies need to be performed in the future for more concrete conclusions.

Prediction of atheromatic plaque evolution in carotids using features extracted from the arterial geometry.

Knowing the arterial geometry might be helpful in the assessment of a plaque rupture event. We present a proof of concept study implementing a novel method which can predict the evolution in time of the atheromatic plaque in carotids using only statistical features which are extracted from the arterial geometry. Four feature selection methods were compared: Quadratic Programming Feature Selection (QPFS), Minimal Redundancy Maximal Relevance (mRMR), Mutual Information Quotient (MIQ) and Spectral Conditional Mutual Information (SPECCMI). The classifier used is the Support Vector Machines (SVM) with linear and Gaussian kernels. The maximum accuracy that was achieved in predicting the variation in the mean value of the Lumen distance from the centerline and the thickness was 71.2% and 70.7% respectively.