- 作者列表："Bikmazer A","Orengul AC","Buyukdeniz A","Okur FV","Gokdemir Y","Perdahli Fis N
AIM:We aimed to evaluate the coping styles and social support perceived by the children with two different chronic diseases (cancer and bronchiectasis), their mothers' coping styles and compare them with a control group without any chronic physical or psychiatric disorder. METHODS:Our sample consisted of 114 children and adolescents, with an age range from 9 to 15 years. The data were collected by using schedule for affective disorders and schizophrenia for school-age children-present and lifetime version, kid-coping orientation to problems experienced (Kid-COPE), social support appraisals scale (SSAS), and COPE. RESULTS:All three groups were similar with respect to age and sex distribution. Around 50% to 60% of the children in both patient groups had a psychiatric diagnosis. Remarkably, 30% of the children had an internalizing disorder. The most commonly used coping style by the mothers was religious coping in all groups. Kid-COPE scores did not significantly differ between groups. The scores on Family and Friend subscales of SSAS in the bronchiectasis group were significantly lower when compared with those of participants in hematology-oncology and control groups. CONCLUSION:Chronic medical illnesses may have a similar psychological impact on children regardless of disease-specific clinical presentations and outcomes. Future studies need to focus on identifying protective and risk factors that potentially mediate psychosocial well-being.
目的: 我们旨在评估两种不同慢性病 (癌症和支气管扩张) 儿童的应对方式和社会支持。他们母亲的应对方式，并与没有任何慢性躯体或精神疾病的对照组进行比较。 方法: 我们的样本包括 114 名儿童和青少年，年龄范围从 9 岁到 15 岁。采用学龄期儿童情感障碍和精神分裂症量表收集数据-现在和一生版本，kid-对经历的问题的应对取向 (Kid-COPE), 社会支持评估量表 (SSAS) 和 COPE。 结果: 三组在年龄和性别分布方面均相似。两组患者中大约有 50% ~ 60% 的儿童有精神病诊断。值得注意的是，30% 的儿童有内在化障碍。在所有群体中，母亲最常用的应对方式是宗教应对。Kid-COPE 评分在组间无显著差异。支气管扩张组 SSAS 的家庭和朋友分量表的评分显著低于血液肿瘤组和对照组的参与者。 结论: 无论疾病特异性临床表现和结果如何，慢性医学疾病可能对儿童产生类似的心理影响。未来的研究需要集中于确定可能介导社会心理健康的保护性和风险因素。
METHODS:Background Dye localization is a useful method for the resection of unidentifiable small pulmonary lesions. This study compares the transbronchial route with augmented fluoroscopic bronchoscopy (AFB) and conventional transthoracic CT-guided methods for preoperative dye localization in thoracoscopic surgery. Methods Between April 2015 and March 2019, a total of 231 patients with small pulmonary lesions who received preoperative dye localization via AFB or percutaneous CT-guided technique were enrolled in the study. A propensity-matched analysis, incorporating preoperative variables, was used to compare localization and surgical outcomes between the two groups. Results After matching, a total of 90 patients in the AFB group ( N = 30) and CT-guided group ( N = 60) were selected for analysis. No significant difference was noted in the demographic data between both the groups. Dye localization was successfully performed in 29 patients (96.7%) and 57 patients (95%) with AFB and CT-guided method, respectively. The localization duration (24.1 ± 8.3 vs. 21.4 ± 12.5 min, p = 0.297) and equivalent dose of radiation exposure (3.1 ± 1.5 vs. 2.5 ± 2.0 mSv, p = 0.130) were comparable in both the groups. No major procedure-related complications occurred in either group; however, a higher rate of pneumothorax (0 vs. 16.7%, p = 0.029) and focal intrapulmonary hemorrhage (3.3 vs. 26.7%, p = 0.008) was noted in the CT-guided group. Conclusion AFB dye marking is an effective alternative for the preoperative localization of small pulmonary lesions, with a lower risk of procedure-related complications than the conventional CT-guided method.
METHODS:Background The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes. Machine learning may also be able to enhance and augment anesthesia clinical procedures such as airway management. In this study, we sought to develop a machine learning algorithm that could classify vocal cords and tracheal airway anatomy real-time during video laryngoscopy or bronchoscopy as well as compare the performance of three novel convolutional networks for detecting vocal cords and tracheal rings. Methods Following institutional approval, a clinical dataset of 775 video laryngoscopy and bronchoscopy videos was used. The dataset was divided into two categories for use for training and testing. We used three convolutional neural networks (CNNs): ResNet, Inception and MobileNet. Backpropagation and a mean squared error loss function were used to assess accuracy as well as minimize bias and variance. Following training, we assessed transferability using the generalization error of the CNN, sensitivity and specificity, average confidence error, outliers, overall confidence percentage, and frames per second for live video feeds. After the training was complete, 22 models using 0 to 25,000 steps were generated and compared. Results The overall confidence of classification for the vocal cords and tracheal rings for ResNet, Inception and MobileNet CNNs were as follows: 0.84, 0.78, and 0.64 for vocal cords, respectively, and 0.69, 0.72, 0.54 for tracheal rings, respectively. Transfer learning following additional training resulted in improved accuracy of ResNet and Inception for identifying the vocal cords (with a confidence of 0.96 and 0.93 respectively). The two best performing CNNs, ResNet and Inception, achieved a specificity of 0.985 and 0.971, respectively, and a sensitivity of 0.865 and 0.892, respectively. Inception was able to process the live video feeds at 10 FPS while ResNet processed at 5 FPS. Both were able to pass a feasibility test of identifying vocal cords and tracheal rings in a video feed. Conclusions We report the development and evaluation of a CNN that can identify and classify airway anatomy in real time. This neural network demonstrates high performance. The availability of artificial intelligence may improve airway management and bronchoscopy by helping to identify key anatomy real time. Thus, potentially improving performance and outcomes during these procedures. Further, this technology may theoretically be extended to the settings of airway pathology or airway management in the hands of experienced providers. The researchers in this study are exploring the performance of this neural network in clinical trials.
METHODS:BACKGROUND:The optimal mode of delivering topical anesthesia during flexible bronchoscopy remains unknown. This article compares the efficacy and safety of nebulized lignocaine, lignocaine oropharyngeal spray, or their combination. METHODS:Consecutive subjects were randomized 1:1:1 to receive nebulized lignocaine (2.5 mL of 4% solution, group A), oropharyngeal spray (10 actuations of 10% lignocaine, group B), or nebulization (2.5 mL, 4% lignocaine) and two actuations of 10% lignocaine spray (group C). The primary outcome was the subject-rated severity of cough according to a visual analog scale. The secondary outcomes included bronchoscopist-rated severity of cough and overall procedural satisfaction on a visual analog scale, total lignocaine dose, subject's willingness to undergo a repeat procedure, adverse reactions to lignocaine, and others. RESULTS:A total of 1,050 subjects (median age, 51 years; 64.8% men) were included. The median (interquartile range) score for subject-rated cough severity was significantly lower in group B compared to group C or group A (4 [1-10] vs 11 [4-24] vs 13 [5-30], respectively; P < .001). The bronchoscopist-rated severity of cough was also the least (P < .001), and the overall satisfaction was highest in group B (P < .001). The cumulative lignocaine dose administered was the least in group B (P < .001). A significantly higher proportion of subjects (P < .001) were willing to undergo a repeat bronchoscopy in group B (73.7%) than in groups A (49.1%) and C (59.4%). No lignocaine-related adverse events were observed. CONCLUSIONS:Ten actuations of 10% lignocaine oropharyngeal spray were superior to nebulized lignocaine or their combination for topical anesthesia during diagnostic flexible bronchoscopy. TRIAL REGISTRY:ClinicalTrials.gov; No.: NCT03109392; URL: www.clinicaltrials.gov.