Objectives. The aim of this study was to evaluate the differences in clinical and laboratory features between eosinophilic chronic rhinosinusitis (ECRS) and non-ECRS and to compare diagnostic criteria for ECRS.Methods. We compared clinical features and/or laboratory findings classified as ECRS and non-ECRS according to various diagnostic criteria (histological and clinical). We also analyzed studies to compare endoscopic findings, symptom scores, laboratory findings, and computed tomography (CT) findings between ECRS and non-ECRS.Results. Our search included 55 studies with 6,143 patients. A comparison of clinical features and/or laboratory criteria with histological criteria showed no significant differences in nasal symptom scores and CT scores according to criteria. Serum eosinophil levels showed differences across the criteria, with ECRS consistently characterized by higher serum eosinophil levels than non-ECRS. Among the four criteria, the Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis (JESREC) criteria and tissue eosinophilia (≥70) were associated with decreased olfactory function. In laboratory findings, the eosinophil percentage (standardized mean difference [SMD], 1.561; 95% confidence interval [CI], 1.329–1.794; P<0.001) and eosinophil count (SMD, 1.493; 95% CI, 1.134–1.852; P<0.001) of eosinophils were higher in ECRS than non-ECRS. In clinical findings, nasal symptom scores (SMD, 0.382; 95% CI, 0.156–0.608; P<0.001), endoscopic nasal polyp scores (SMD, 0.581; 95% CI, 0.314–0.848; P<0.001), and olfactory dysfunction (SMD, 0.416; 95% CI, 0.037–0.794; P=0.031) were higher in ECRS than in non-ECRS. With regard to CT findings, the whole-sinus opacification score (SMD, 0.824; 95% CI, 0.588–1.059; P<0.001) was higher in ECRS than in non-ECRS. In particular, there were significant differences in anterior ethmoid sinus and sphenoid sinus opacification.Conclusion. ECRS and non-ECRS differ in their clinical and laboratory features. When histological confirmation is difficult on an outpatient basis, ECRS could be diagnosed using clinical features and/or laboratory findings.

The goal of this meta-analysis study was to perform a systematic review of the literature on the effects of ketamine on postoperative pain following tonsillectomy and adverse effects in children. Two authors independently searched three databases (MEDLINE, SCOPUS, Cochrane) from their inception of article collection to February 2014. Studies that compared preoperative ketamine administration (ketamine groups) with no treatment (control group) or opioid administration (opioid group) where the outcomes of interest were postoperative pain intensity, rescue analgesic consumption, or adverse effects (sedation, nausea and vomiting, bad dream, worsening sleep pattern, and hallucination) 0–24 hours after leaving the operation room were included in the analysis. The pain score reported by the physician during first 4 hours and need for analgesics during 24 hours postoperatively was significantly decreased in the ketamine group versus control group and was similar with the opioid group. In addition, there was no significant difference between ketamine and control groups for adverse effects during 24 hours postoperatively. In the subgroup analyses (systemic and local administration) regarding pain related measurements, peritonsillar infiltration of ketamine was more effective in reducing the postoperative pain severity and need for analgesics. Preoperative administration of ketamine systemically or locally could provide pain relief without side-effects in children undergoing tonsillectomy. However, considering the insufficient evaluation of efficacy of ketamine according to the administration methods and high heterogeneity in some parameters, further clinical trials with robust research methodology should be conducted to confirm the results of this study.

The accuracy of artificial intelligence (AI)-assisted discrimination of oral cancerous lesions from normal mucosa based on mucosal images was evaluated. Two authors independently reviewed the database until June 2022. Oral mucosal disorder, as recorded by photographic images, autofluorescence, and optical coherence tomography (OCT), was compared with the reference results by histology findings. True-positive, true-negative, false-positive, and false-negative data were extracted. Seven studies were included for discriminating oral cancerous lesions from normal mucosa. The diagnostic odds ratio (DOR) of AI-assisted screening was 121.66 (95% confidence interval [CI], 29.60; 500.05). Twelve studies were included for discriminating all oral precancerous lesions from normal mucosa. The DOR of screening was 63.02 (95% CI, 40.32; 98.49). Subgroup analysis showed that OCT was more diagnostically accurate (324.33 vs. 66.81 and 27.63) and more negatively predictive (0.94 vs. 0.93 and 0.84) than photographic images and autofluorescence on the screening for all oral precancerous lesions from normal mucosa. Automated detection of oral cancerous lesions by AI would be a rapid, non-invasive diagnostic tool that could provide immediate results on the diagnostic work-up of oral cancer. This method has the potential to be used as a clinical tool for the early diagnosis of pathological lesions.