DOZ047.03: The functional chewing training for chewing dysfunction in children with repaired esophageal atresia–tracheoesophageal fistula

Abstract Introduction Chewing disorders (CD) may cause restrictions in solid food intake and can be seen in 37% of children with esophageal atresia–tracheoesophageal fistula (EA-TEF).1 The Functional Chewing Training (FuCT) is a holistic approach to improve chewing function (CF) in children. The study aimed to evaluate the effects of FuCT on CF in children with EA-TEF. Materials and Methods Twenty children with CD were included. Patients received 12 weeks of FuCT, including impairment-based and adaptive components. Chewing performance level was scored with the Karaduman Chewing Performance Scale (KCPS), and tolerated food texture was determined by the International Dysphagia Diet Standardization Initiative (IDDSI). The baseline and final levels of KCPS and IDDSI were compared to evaluate the effects of FuCT on CF. Results 45% of cases were isolated-EA and 55% were EA-distal TEF with a median age of 31 (min = 25, max = 84) months, of which 65% (n = 13) were male. Baseline evaluation showed that 12 cases were in level-1, 6 cases in level-3 and 2 cases in level-4 according to the KCPS. Eight children with CD (40%) had IDDSI level-3 and 12 (60%) had level-7. There was a significant improvement in KCPS scores and IDDSI scores after 12 weeks of training (P < 0.01, P = 0.005, respectively). KCPS scores showed level-0 in 15 cases, and level-1 in 5 cases. All children had IDSSI level-7. Conclusions The FuCT is an effective method to improve chewing function in children EA-TEF who had CD.

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Chewing Function in Children with Repaired Esophageal Atresia–Tracheoesophageal Fistula

European Journal of Pediatric Surgery ◽

10.1055/s-0037-1608930 ◽

2017 ◽

Vol 28 (06) ◽

pp. 534-538 ◽

Cited By ~ 5

Author(s):

Selen Serel Arslan ◽

Numan Demir ◽

Aynur Karaduman ◽

Feridun Tanyel ◽

Tutku Soyer

Keyword(s):

Esophageal Atresia ◽

Tracheoesophageal Fistula ◽

Primary Repair ◽

Oral Feeding ◽

Feeding Problems ◽

Food Texture ◽

Significant Difference ◽

Level 3 ◽

Level I ◽

Chewing Disorder

Introduction Feeding problems are common in children with esophageal atresia and tracheoesophageal fistula (EA–TEF); however, chewing disorders, which may cause inability to intake solid food, have not been evaluated. Therefore, we aimed to evaluate the chewing function in children with repaired EA–TEF. Materials and Methods Age, sex, the type of atresia, the type of repair, and the time to start oral feeding were recorded. The level of the chewing performance was scored according to the Karaduman Chewing Performance Scale (KCPS). The International Dysphagia Diet Standardization Initiative (IDDSI) was used to determine the tolerated food texture in children. Results A group of 30 patients were included, of which 53.3% was male. The percentages of the isolated-EA and that of the EA–distal TEF were 40% and 60%, respectively. The median value for the time to start oral feeding was 4.5 weeks (min = 1, max = 72). Eleven (36.7%) children had chewing disorder. The KCPS scores showed level I in six cases, level III in four cases, and level IV in one case. Five children with chewing disorder had IDDSI level 3 and six had level 7, along with the sensation of stuck food. We found no significant difference between the KCPS scores according to the repair type (p = 0.07). The median values of the KCPS scores of children with primary repair, delayed repair, and colon interposition were 0 (min = 0, max = 4), 0.5 (min = 0, max = 3), 2 (min = 0, max = 3), respectively. A significant positive correlation was found between the time to start oral feeding and the KCPS scores (r = 0.63, p = 0.001). Conclusion Chewing disorders can be observed in children with EA–TEF, and the type of repair and the delay in oral feeding may be related to chewing disorder. Therapeutic maneuvers are needed to improve the chewing function in children with EA–TEF.

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V7 Project Restart’; Safe resumption of elective orthopaedic surgery following the Covid-19 pandemic

BJS Open ◽

10.1093/bjsopen/zrab034.006 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

Author(s):

Marriam Ahmed ◽

Hariharan Subbiah Ponniah ◽

Thomas Edwards ◽

Alexander Liddle ◽

Justin Cobb ◽

...

Keyword(s):

Healthcare Workers ◽

Elective Surgery ◽

Holistic Approach ◽

Evidence Based ◽

Short Stay ◽

Rt Pcr ◽

Level 3 ◽

Level 1 ◽

The Uk ◽

Surgical Unit

Abstract Introduction The Covid-19 pandemic resulted in nearly 2 million patients being put on waiting lists for elective procedures in the UK. We aim to describe how the COVID-19 Algorithm for Resuming Elective Surgery (CARES) was used to allocate patients to elective theatre lists while factoring in patient safety, risk to healthcare workers and, protection of resources. Methodology A multidisciplinary team was employed with the task of using CARES to allocate theatre slots to 1169 patients on the waiting list. CARES was used in conjunction with an evidence-based scale for procedural urgency (Levels 1-4) to stratify patients and list them for surgery at one of three ‘COVID-light’ sites i.e. 1. With HDU/ITU access, specialist staff, and equipment, 2. An NHS short-stay surgical unit, 3. A private surgical unit. Incidence of post-operative Covid-19 infection was assessed by looking at positive Covid-19 RT-PCR or CT Chest with characteristic findings performed within 2 weeks of the surgery. Results 118 cases were deemed to be Priority 1/2, 222 were Level 3, and 808 were Level 4. In 6 weeks, 355 surgeries were performed, with Urgent and Level 1/2 cases performed soonest (mean 18 days, p < 0.001). 33 high-risk/complex/paediatric patients had surgery at Site 1 and the rest at Sites 2 and 3. No patients contracted COVID-19 within 2 weeks of surgery. Conclusion CARES’ holistic approach enabled equitable and safe resumption of arthroplasty during the pandemic, by stratification and creation of COVID-light sites. It could be applied internationally and across sub-specialties. Introduction The Covid-19 pandemic resulted in nearly 2 million patients being put on waiting lists for elective procedures in the UK. We aim to describe how the COVID-19 Algorithm for Resuming Elective Surgery (CARES) was used to allocate patients to elective theatre lists while factoring in patient safety, risk to healthcare workers and, protection of resources. Methodology A multidisciplinary team was employed with the task of using CARES to allocate theatre slots to 1169 patients on the waiting list. CARES was used in conjunction with an evidence-based scale for procedural urgency (Levels 1-4) to stratify patients and list them for surgery at one of three ‘COVID-light’ sites i.e. 1. With HDU/ITU access, specialist staff, and equipment, 2. An NHS short-stay surgical unit, 3. A private surgical unit. Incidence of post-operative Covid-19 infection was assessed by looking at positive Covid-19 RT-PCR or CT Chest with characteristic findings performed within 2 weeks of the surgery. Results 118 cases were deemed to be Priority 1/2, 222 were Level 3, and 808 were Level 4. In 6 weeks, 355 surgeries were performed, with Urgent and Level 1/2 cases performed soonest (mean 18 days, p < 0.001). 33 high-risk/complex/paediatric patients had surgery at Site 1 and the rest at Sites 2 and 3. No patients contracted COVID-19 within 2 weeks of surgery. Conclusion CARES’ holistic approach enabled equitable and safe resumption of arthroplasty during the pandemic, by stratification and creation of COVID-light sites. It could be applied internationally and across sub-specialties.

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Using a Miniature Stapler to Divide a Mucosal Bridge at the Anastomosis after Gastric Pull-up for Iatrogenic Tracheoesophageal Fistula

European Journal of Pediatric Surgery Reports ◽

10.1055/s-0039-1678755 ◽

2018 ◽

Vol 06 (01) ◽

pp. e108-e110

Author(s):

Takafumi Kawano ◽

Oliver Muensterer

Keyword(s):

Esophageal Atresia ◽

Tracheoesophageal Fistula ◽

Gastroesophageal Junction ◽

Solid Food ◽

One Year ◽

Gastric Pull Up

AbstractWe report the first use of a miniature stapler to divide a mucosal bridge at the gastroesophageal junction after complex esophageal atresia (EA) repair. A 4-year-old girl was referred to our center after treatment of EA elsewhere. On our initial enodoscopy, a large iatrogenic tracheoesophageal fistula had formed, prompting us to perform a tracheoplasty and gastric interposition. One year after recovery, she had dysphagia with solid food. Upon endoscopy, a mucosal bridge was noted at the gastroesophageal anastomosis. This bridge was divided under endoscopy using a 5 mm miniature stapler. No complications were noted. Upon follow-up, she had no more complaints with solid food. Our report shows that the mucosal bridges may cause dysphagia after EA repair and can be safely divided using a miniature stapler.

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Anwendung des 10-Gruppenklassifikationssystems nach Robson für den Vergleich eines Perinatalzentrums Level 1 mit einem Level-3-Zentrum

10.1055/s-0040-1717961 ◽

2020 ◽

Author(s):

C Pulvermacher ◽

P van de Vondel ◽

L Gerzen ◽

U Gembruch ◽

W Merz

Keyword(s):

Level 3 ◽

Level 1

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EVALUASI PELATIHAN DI PT. PUPUK KUJANG CIKAMPEK

PSYCHOPEDIA : Jurnal Psikologi Universitas Buana Perjuangan Karawang ◽

10.36805/psikologi.v3i2.714 ◽

2018 ◽

Vol 3 (2) ◽

Author(s):

Lania Muharsih ◽

Ratih Saraswati

Keyword(s):

Training Evaluation ◽

Work Productivity ◽

Learning Behavior ◽

Level 3 ◽

Level 1 ◽

Four Levels ◽

A Company ◽

Level 2 ◽

Evaluation Sheet ◽

Assessment Factor

This study aims to determine the training evaluation at PT. Kujang Fertilizer. PT. Pupuk Kujang is a company engaged in the field of petrochemicals. Evaluation sheet of PT. Fertilizer Kujang is made based on Kirkpatrick's theory which consists of four levels of evaluation, namely reaction, learning, behavior, and results. At level 1, namely reaction, in the evaluation sheet is in accordance with the theory of Kirkpatrick, at level 2 that is learning should be held pretest and posttest but only made scale. At level 3, behavior, according to theory, but on assessment factor number 3, quantity and work productivity should not need to be included because they are included in level 4. At level 4, that is the result, here is still lacking to get a picture of the results of the training that has been carried out because only based on answers from superiors without evidence of any documents. Keywords: Training Evaluation, Kirkpatrick Theory. Penelitian ini bertujuan mengetahui evaluasi training di PT. Pupuk Kujang. PT. Pupuk Kujang merupakan perusahaan yang bergerak di bidang petrokimia. Lembar evaluasi PT. Pupuk Kujang dibuat berdasarkan teori Kirkpatrick yang terdiri dari empat level evaluasi, yaitu reaksi, learning, behavior, dan hasil. Pada level 1 yaitu reaksi, di lembar evaluasi tersebut sudah sesuai dengan teori dari Kirkpatrick, pada level 2 yaitu learning seharusnya diadakan pretest dan posttest namun hanya dibuatkan skala. Pada level 3 yaitu behavior, sudah sesuai teori namun pada faktor penilaian nomor 3 kuantitas dan produktivitas kerja semestinya tidak perlu dimasukkan karena sudah termasuk ke dalam level 4. Pada level 4 yaitu hasil, disini masih sangat kurang untuk mendapatkan gambaran hasil dari pelatihan yang sudah dilaksanakan karena hanya berdasarkan dari jawaban atasan tanpa bukti dokumen apapun. Kata kunci: Evaluasi Pelatihan, Teori Kirkpatrick.

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905 How to prioritise patients and safely resume elective surgery during the Covid-19 pandemic

British Journal of Surgery ◽

10.1093/bjs/znab134.095 ◽

2021 ◽

Vol 108 (Supplement_2) ◽

Author(s):

H Subbiah Ponniah ◽

M Ahmed ◽

T Edwards ◽

J Cobb ◽

E Dean ◽

...

Keyword(s):

Patient Safety ◽

High Risk ◽

Orthopaedic Surgery ◽

Elective Surgery ◽

Paediatric Patients ◽

Level 3 ◽

Level 1 ◽

Surgical Unit

Abstract Introduction There are now over 2.5 million NHS patients awaiting elective surgery, with the most in orthopaedics. We present an algorithm and results for safely and equitably restarting surgery at COVID-light sites. Method An MDT applied the COVID-19 Algorithm for Resuming Elective Surgery (CARES) on 1169 patients awaiting elective orthopaedic surgery. It assessed safety, procedural efficacy, and biopsychosocial factors, to prioritise patients. They were assigned to five categories and underwent surgery at one of three COVID-light sites (1. access to HDU/ITU/Paediatrics/specialist equipment, 2. an NHS elective surgical unit and 3. a private elective surgical unit). Results 21 ‘Urgent’ patients received expedited care; 118 were Level 1/2; 222 were Level 3; 808 were Level 4. In 6 weeks, 355 surgeries were performed, with Urgent and Level 1/2 cases performed soonest (mean 18 days, p < 0.001). 33 high-risk/complex/paediatric patients had surgery at Site 1 and the rest at Sites 2 and 3. No patients contracted COVID-19 within 2 weeks of surgery. Conclusions We validated a widely generalisable model to facilitate resumption of elective surgery in COVID-light sites. It enabled surgery for patients in most suffering, undergoing the most efficacious procedures and/or at highest risk of deterioration, without compromising patient-safety.

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An Atmospheric Visibility Grading Method Based on Ensemble Learning and Stochastic Weight Average

Atmosphere ◽

10.3390/atmos12070869 ◽

2021 ◽

Vol 12 (7) ◽

pp. 869

Author(s):

Xiuguo Zou ◽

Jiahong Wu ◽

Zhibin Cao ◽

Yan Qian ◽

Shixiu Zhang ◽

...

Keyword(s):

Ensemble Learning ◽

Traffic Accidents ◽

Feature Sets ◽

Atmospheric Visibility ◽

Average Accuracy ◽

Level 3 ◽

Visual Characteristics ◽

Level 1 ◽

Grading Model ◽

Level 2

In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, and complicated steps, this paper proposed an ensemble learning method for atmospheric visibility grading based on deep neural network and stochastic weight averaging. An experiment was conducted using the scene of an expressway, and three visibility levels were set, i.e., Level 1, Level 2, and Level 3. Firstly, the EfficientNet was transferred to extract the abstract features of the images. Then, training and grading were performed on the feature sets through the SoftMax regression model. Subsequently, the feature sets were ensembled using the method of stochastic weight averaging to obtain the atmospheric visibility grading model. The obtained datasets were input into the grading model and tested. The grading model classified the results into three categories, with the grading accuracy being 95.00%, 89.45%, and 90.91%, respectively, and the average accuracy of 91.79%. The results obtained by the proposed method were compared with those obtained by the existing methods, and the proposed method showed better performance than those of other methods. This method can be used to classify the atmospheric visibility of traffic and reduce the incidence of traffic accidents caused by atmospheric visibility.

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