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"Imaging in pediatric pulmonology is a definitive reference to imaging and differential diagnosis for pediatric pulmonology. Diseases and disorders seen in everyday clinical practice are featured, including infections, developmental disorders, airway abnormalities, diffuse lung diseases, focal lung diseases, and lung tumors. Organized to support the clinical thought process, the text begins with a series of clinical algorithms that provide a starting point for formulating a diagnosis. The physician will be able to identify the differentials by symptom complex and accordingly determine what test would be effective and how to proceed. The balance of the book is image-based and presents a comprehensive, multi-modality approach, with an emphasis on plain film and cross-sectional imaging. The imaging sections are correlated with pathology and clinical findings to help readers learn what the modality of choice can enable them to see. "

"Efficiency is the key word at the heart of the concept for this diagnostic atlas - efficiency of presentation and diagnostic efficiency achieved through its use. The contents are arranged in chapters according to organ or body part. Each chapter begins with a list of the radiographic findings referring to the relevant table/​page number for that finding. The text within these tables is presented under three columns: possible diagnoses, radiographic findings, and general comments. Corresponding sample images of the highest quality are then provided for the more common conditions throughout. Rare diseases are marked with asterisks and are illustrated where appropriate. The hard-pressed radiologist will welcome this work as an efficient and reliable guide to the interpretation of pediatric images"--Provided by publisher"

"Summary:Offers the coverage and unparalleled guidance in various areas of pediatric radiology. This title focuses on the core issues that you need to understand protocols and sequences, and know what techniques are appropriate for given clinical situations."

"Consists of diagnoses of all common disorders of the pediatric nervous system and many that are not common. For each diagnosis, information is included concerning the clinical presentation(s) of affected patients, the best sequences to perform for imaging analysis, what each imaging sequence is expected to show (in both common and uncommon presentations), and examples of images showing the key features. In addition, information is included concerning the pathophysiology and pathology of the disorders being discussed, and some basic information concerning the causative genes (when appropriate). In addition to the diagnoses, the book contains introductory chapters in multiple sections that give background on basic embryology, anatomy, and physiology as well as typical imaging features of normal structures in areas being imaged. Put together, the contents of the book make it useful for readers of many different backgrounds and at nearly all stages of training as well as practicing health professionals"

"In Essentials of sleep medicine : an approach for clinical pulmonology, a concise, evidence-based review of sleep medicine for the pulmonologist is presented. Providing a focused, scientific basis for the effects of sleep on human physiology, especially cardiac and respiratory physiology, chapters also outline a differential diagnosis for common sleep complaints and an evidence-based approach to diagnosis and management. This includes a review of the current standards of practice and of emerging technology and unresolved issues awaiting further research. Each chapter includes a summary of current research and outlines future research directions and issues. In all, Essentials of sleep medicine : an approach for clinical pulmonology provides a clear diagnostic and management program for all the different sleep disorders, with a major focus on respiratory disorders of sleep, and includes key points and summaries. n-depth review of sleep medicine."

"Penelitian ini bertujuan untuk mendapatkan kombinasi parameter yang optimal dalam simulasi pemeriksaan kranial, toraks, dan abdomen menggunakan sistem digital radiography (DR). Optimasi dilakukan menggunakan phantom in-house dengan objek kontras pada DR Siemens Luminos Agile Max. Pasien pediatrik dipisahkan menjadi empat kelompok usia; grup A (0-1 tahun), grup B (1-5 tahun), grup C (5-10 tahun), dan grup D (10-15 tahun). Kombinasi lapisan PMMA dan cork dengan ketebalan total yang berbeda digunakan untuk mensimulasikan pasien yang termasuk dalam setiap kelompok usia untuk wilayah anatomis yang berbeda (kranial, toraks, dan abdomen). Optimasi dilakukan dalam tiga langkah; kVp, diikuti oleh mAs, dan kemudian optimasi filter tambahan. Semua langkah optimasi dilakukan berdasarkan nilai FOM (figure of merit) yang dihitung sebagai rasio SDNR (signal difference to noise ratio) kuadrat dan entrance surface dose dengan FOM tertinggi yang mewakili kondisi optimum. Hasil dari optimasi ini dievaluasi berdasarkan FOM tertinggi yang dihasilkan dari setiap eksposi. Adapun MTF dan CV digunakan sebagai parameter pembanding terhadap nilai FOM yang rancu. Dalam pemeriksaan kranial, FOM tertinggi dihasilkan oleh faktor eksposi 44 kV, 3.2 mAs, dan 0 mmCu atau tanpa filter (A), 46 kV, 5.6 mAs, dan 0.1 mmCu (B), 49 kV, 7.1 mAs, dan 0.2 mmCu (C) dan 50 kV, 9 mAs, dan 0.1 mmCu (D). Untuk pemeriksaan toraks, nilai FOM tertinggi dihasilkan oleh faktor eksposi 45 kV, 2,5 mAs, dan 0,2 mmCu (A), 45 kV, 4 mAs, dan 0.2 mmCu (B), 46 kV, 5.6 mAs, dan 0.2 mmCu (C), dan 47 kV, 6.3 mAs, dan 0.2 mmCu (D). Untuk pemeriksaan abdomen, nilai FOM tertinggi dihasilkan oleh faktor eksposi 48 kV, 4 mAs, dan 0.1 mmCu (A), 50 kV, 6.3 mAs, dan 0.2 mmCu (B), 53.5 kV, 8 mAs, dan 0 mmCu (C), dan 58.5 kV, 8 mAs, dan 0 mmCu (D).

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This study was aimed to obtain optimum parameter combination in simulated cranial, thorax, and abdominal examinations using digital radiography (DR) systems. Optimization was performed using in-house phantom with contrast objects on Siemens Luminos Agile Max DR. Paediatric patients were separated into four age groups; group A (0-1 year), group B (1-5 years), group C (5-10 years), and group D (10-15 years). Slab phantoms consisted of PMMA and cork with different total thickness were used to simulate patients belonging to each age group for different anatomical region (cranial, thorax, and abdomen). Optimization were performed in three steps; first kVp, followed by mAs, and then additional filter optimization. All the steps of optimization were performed based on FOM (figure of merit) values calculated as ratio of squared SDNR (signal difference to noise ratio) and entrance surface dose with the highest FOM representing the optimum condition.

The results of this optimization were evaluated based on the highest FOM generated from each exposure. For this DR, optimum parameters (i.e. highest FOM) are different for each age group and anatomical region. In cranial examination, the highest FOM are generated by exposure factors of 44 kV, 3.2 mAs, and 0 mmCu filter (A), 46 kV, 5.6 mAs, and 0.1 mmCu filter (B), 49 kV, 7.1 mAs, and 0.2 mmCu filter (C) and 50 kV, 9 mAs, and 0.1 mmCu filter (D). For thorax examination, the highest FOM value is generated by exposure factor 45 kV, 2.5 mAs, and 0.2 mmCu (A), 45 kV, 4 mAs, and 0.2 mmCu (B), 46 kV, 5.6 mAs, and 0.2 mmCu (C), and 47 kV, 6.3 mAs, and 0.2 mmCu (D). For abdominal examination, the highest FOM value is produced by exposure factor 48 kV, 4 mAs, and 0.1 mmCu (A), 50 kV, 6.3 mAs, and 0.2 mmCu (B), 53.5 kV, 8 mAs, and 0 mmCu (C), and 58.5 kV, 8 mAs, and 0 mmCu (D)."

"ABSTRAK Latar Belakang: Banyaknya jumlah kasus ROP yang terlambat dirujuk ke RSCM-Kirana menunjukkan bahwa penanganan ROP di Indonesia masihmerupakan tantangan. Keterlambatan diagnosis ROP yang ditemukan di RSCM tersebut dapat disebabkan oleh terbatasnya dokter mata ahli retina dan pediatric ophthalmologist (PO) dan kurangnya pengetahuan dokter mata umum dalammendiagnosis ROP. Pengetahuan dan keterampilan mendiagnosis ROPmerupakan salah satu mata ajar dalam program pendidikan dokter spesialis matadi FKUI. Data mengenai seberapa baik pengetahuan PPDS tersebut dalammendiagnosis ROP belum tersedia. Tujuan: Mengevaluasi kemampuan PPDS mata dalam mendiagnosis ROPberdasarkan pembacaan hasil foto wide field digital retinal imaging (WFDRI)bayi prematur. Metode: Sebanyak 25 set foto WFDRI dibaca dan diinterpretasi oleh 15 subjekPPDS mata, dan digolongkan ke dalam 4 klasifikasi: tidak ada ROP, ROP ringan,ROP tipe 2, dan ROP yang memerlukan terapi. Pembacaan oleh subjek tersebutdihitung nilai kesesuaiannya (Kappa) terhadap pembacaan oleh 3 konsultan POsebagai referensi, kemudian ditentukan tingkat kesesuaian berdasarkanpenggolongan nilai Kappa. Tingkat kesesuaian yang diharapkan adalah ?sangatbaik?, khusus untuk penentuan ROP perlu terapi, nilai Kappa yang diharapkanadalah 1,00. Hasil: Dalam penentuan adanya ROP, seluruh subjek memiliki tingkat kesesuaian?sangat baik? (Kappa 1,00); dalam penentuan ROP ringan atau lebih berat, hanya1 dari 15 subjek memiliki tingkat kesesuaian ?sangat baik?, 9 subjek memilikikesesuaian ?baik?, dan 5 subjek memiliki kesesuian ?sedang? (Kappa 0,65+0,15);dalam penentuan ROP tipe 2 atau lebih berat, 10 dari 15 subjek memiliki tingkatkesesuaian ?sangat baik?, 3 subjek memiliki kesesuaian ?baik?, dan 2 subjekmemiliki kesesuaian ?sedang? (Kappa 0,45-1,00); dalam penentuan ROP yangmemerlukan terapi, hanya 7 dari 15 subjek yang memiliki nilai Kappa 1,00,namun 12 dari 15 subjek memiliki tingkat kesesuaian ?sangat baik?, dan 3 subjekmemiliki kesesuaian ?baik? (Kappa 0,75-1,00). Dalam penentuan zona danstadium, hanya sebagian kecil subjek yang memiliki tingkat kesesuaian ?sangatbaik? (Kappa 0,35-0,81 dan 0,32-0,91); sedangkan dalam penentuan plus disease,hanya 6 dari 15 subjek yang memiliki nilai Kappa 1,00. Kesimpulan: Kemampuan PPDS mata dalam mendiagosis ROP belumseluruhnya mencapai target yang diharapkan. ABSTRACT Background: Numerous late-stage ROP cases that referred to CiptoMangunkusumo Hospital (Kirana) showed us that ROP management in Indonesiais still a challenge. The delayed management might be caused by limited numberof vitreoretinal specialist or pediatric ophthalmologist, and inadequate diagnosticknowledge of ROP of the general ophthalmologists. In condition of limitednumber of vitreoretinal and pediatric ophthalmologists, the generalophthalmologists are suggested to be taking part in ROP screening program. InFKUI, ROP screening has been a part of residency training program, however,there was no available data of ROP diagnostic knowledge of the residents. Purpose: To measure agreement of image-based ROP diagnosis betweenophthalmology residents and pediatric ophthalmologist. Methods: Twenty-five sets of retinal photographs of premature infants wereinterpreted by 15 ophthalmology residents and pediatric ophthalmologists, andclassified into 4 categories: no ROP, mild ROP, type 2 ROP, and treatmentrequiringROP.Agreementsaremeasuredfordiagnosisclassification,stage,zone,andplus disease detection. Level of agreement was measured based on Kappavalue of each subjects. The expected level of agreement for each category was?very good agreement?. For treatment-requiring ROP and plus disease, theexpected Kappa value was 1.00. Results: For detection of no ROP, agreement of all subjects was ?very good?(Kappa 1,00); for detection of mild or worse ROP, only 1 of 15 subjects has ?verygood agreement?, 9 of 15 subjects have ?good agreement?, and 5 subjects have?moderate agreement? (Kappa 0,65+0,15); for detection of type 2 or worse ROP,10 of 15 subjects have ?very good agreement?, 3 subjects have ?good agreement?,and 2 subjects have ?moderate agreement? (Kappa 0,45-1,00); for detection ofrequiring-therapy ROP, only 7 of 15 subjects that have Kappa value of 1.00,however, 12 of 15 subjects have ?very good agreement?, and only 3 subjects have?good agreement? (Kappa 0,75-1,00). For detection of stage and zone of ROP,only a little number of subjects have ?very good agreement? (Kappa 0.35-0.81,and 0.32-0.91, respectively); and for plus disease detection, only 6 of 15 subjectshave Kappa value of 1.00. Conclusion: Agreement of image-based ROP diagnosis between ophthalmologyresidents and pediatric ophthalmologist has not achieved the expected target yet. ;Background: Numerous late-stage ROP cases that referred to CiptoMangunkusumo Hospital (Kirana) showed us that ROP management in Indonesiais still a challenge. The delayed management might be caused by limited numberof vitreoretinal specialist or pediatric ophthalmologist, and inadequate diagnosticknowledge of ROP of the general ophthalmologists. In condition of limitednumber of vitreoretinal and pediatric ophthalmologists, the generalophthalmologists are suggested to be taking part in ROP screening program. InFKUI, ROP screening has been a part of residency training program, however,there was no available data of ROP diagnostic knowledge of the residents. Purpose: To measure agreement of image-based ROP diagnosis betweenophthalmology residents and pediatric ophthalmologist. Methods: Twenty-five sets of retinal photographs of premature infants wereinterpreted by 15 ophthalmology residents and pediatric ophthalmologists, andclassified into 4 categories: no ROP, mild ROP, type 2 ROP, and treatmentrequiringROP.Agreementsaremeasuredfordiagnosisclassification,stage,zone,andplus disease detection. Level of agreement was measured based on Kappavalue of each subjects. The expected level of agreement for each category was?very good agreement?. For treatment-requiring ROP and plus disease, theexpected Kappa value was 1.00. Results: For detection of no ROP, agreement of all subjects was ?very good?(Kappa 1,00); for detection of mild or worse ROP, only 1 of 15 subjects has ?verygood agreement?, 9 of 15 subjects have ?good agreement?, and 5 subjects have?moderate agreement? (Kappa 0,65+0,15); for detection of type 2 or worse ROP,10 of 15 subjects have ?very good agreement?, 3 subjects have ?good agreement?,and 2 subjects have ?moderate agreement? (Kappa 0,45-1,00); for detection ofrequiring-therapy ROP, only 7 of 15 subjects that have Kappa value of 1.00,however, 12 of 15 subjects have ?very good agreement?, and only 3 subjects have?good agreement? (Kappa 0,75-1,00). For detection of stage and zone of ROP,only a little number of subjects have ?very good agreement? (Kappa 0.35-0.81,and 0.32-0.91, respectively); and for plus disease detection, only 6 of 15 subjectshave Kappa value of 1.00. Conclusion: Agreement of image-based ROP diagnosis between ophthalmologyresidents and pediatric ophthalmologist has not achieved the expected target yet. ;Background: Numerous late-stage ROP cases that referred to CiptoMangunkusumo Hospital (Kirana) showed us that ROP management in Indonesiais still a challenge. The delayed management might be caused by limited numberof vitreoretinal specialist or pediatric ophthalmologist, and inadequate diagnosticknowledge of ROP of the general ophthalmologists. In condition of limitednumber of vitreoretinal and pediatric ophthalmologists, the generalophthalmologists are suggested to be taking part in ROP screening program. InFKUI, ROP screening has been a part of residency training program, however,there was no available data of ROP diagnostic knowledge of the residents. Purpose: To measure agreement of image-based ROP diagnosis betweenophthalmology residents and pediatric ophthalmologist. Methods: Twenty-five sets of retinal photographs of premature infants wereinterpreted by 15 ophthalmology residents and pediatric ophthalmologists, andclassified into 4 categories: no ROP, mild ROP, type 2 ROP, and treatmentrequiringROP.Agreementsaremeasuredfordiagnosisclassification,stage,zone,andplus disease detection. Level of agreement was measured based on Kappavalue of each subjects. The expected level of agreement for each category was?very good agreement?. For treatment-requiring ROP and plus disease, theexpected Kappa value was 1.00. Results: For detection of no ROP, agreement of all subjects was ?very good?(Kappa 1,00); for detection of mild or worse ROP, only 1 of 15 subjects has ?verygood agreement?, 9 of 15 subjects have ?good agreement?, and 5 subjects have?moderate agreement? (Kappa 0,65+0,15); for detection of type 2 or worse ROP,10 of 15 subjects have ?very good agreement?, 3 subjects have ?good agreement?,and 2 subjects have ?moderate agreement? (Kappa 0,45-1,00); for detection ofrequiring-therapy ROP, only 7 of 15 subjects that have Kappa value of 1.00,however, 12 of 15 subjects have ?very good agreement?, and only 3 subjects have?good agreement? (Kappa 0,75-1,00). For detection of stage and zone of ROP,only a little number of subjects have ?very good agreement? (Kappa 0.35-0.81,and 0.32-0.91, respectively); and for plus disease detection, only 6 of 15 subjectshave Kappa value of 1.00. Conclusion: Agreement of image-based ROP diagnosis between ophthalmologyresidents and pediatric ophthalmologist has not achieved the expected target yet. "

"Kejang demam, riwayat keluarga dan pencitraan merupakan faktor-faktor yang dapat memengaruhi klasifikasi epilepsi berdasarkan ILAE 1989. Penentuan jenis klasifikasi berguna untuk penatalaksanaan pasien. Penelitian ini dilakukan dengan menggunakan data rekam medis tahun 1995-2010 Departemen Ilmu Kesehatan Anak RSCM. Penelitian ini merupakan penelitian analitik dengan desain cross-sectional. Data diolah dengan multivariat regresi logistik. Dari hasil penelitian ini, didapat sampel sebanyak 99 orang dengan rincian laki-laki 53,4%, perempuan 46,5%. Pasien terbanyak pada kelompok umur 0-2 tahun 12 bulan (37,4%). Terdapat kebermaknaan yang signifikan pada hubungan antara pencitraan dengan klasifikasi epilepsi (p < 0,001). Tidak terdapat kebermaknaan yang signifikan terhadap hubungan antara riwayat epilepsi keluarga (p = 0,393) dan riwayat kejang demam ( p = 0,161) dengan klasifikasi epilepsi. Pencitraan merupakan faktor yang berpengaruh paling besar (OR = 16,725) terhadap penentuan jenis klasifikasi epilepsi bila dibandingkan dengan riwayat epilepsi keluarga dan riwayat kejang demam.

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Febrile seizure, family history, and imaging are factors that determine the classification of epilepsy based on ILAE 1989. The classification is important to patient's treatment.This study used medical record from Pediatric Department of RSCM in 1995-2010. This study is a cross-sectional analytic. The data was proceed with multivariate logistic regression. There are 99 sample, 53.4% are male and 46.5% female. The most distribution of patient's age is in 0-3 years (37.4%). There is significant results in correlation between imaging with epilepsy classification (p<0.001) and there are less significant results between family history (p=0.393) and febrile seizure (p=0.161) with epilepsy classification. Imaging is the most powerful factor (OR = 16.725) that contribute to determine classification of epilepsy compared to family history and febrile seizure."