Do you want to publish a course? Click here

Induction of Labor With Unmedicinal Methods ( Via Membrane Stripping)

تحريض المخاض بطرق غير دوائية (عن طريق تسليخ الأغشية الامنيوسية)

2493   0   18   0 ( 0 )
 Publication date 2015
and research's language is العربية
 Created by Shamra Editor




Ask ChatGPT about the research

This study was made at ALASSAD Hospital-TISHREEN University in-LATTAKIA at the Department of Obestetrics and Gynecology in the period between 1/1/2013 and 1/1/2014. The number of patients the study was 190.Including140 cases have been holding membrane stripping and 50 cases without membrane stripping. According to Bishop –Scoring index we have estimated the uterine cervix in patients, then a Membrane- Stripping was made. Rate of response was 79.28% in cases of membrane stripping "then 54%in cases " without membrane striping ". In most cases of response ,we need to do the membrane stripping twice.Top of response was obtained when Bishop –Scoring index was between 4- 6 (Moderate ripening of the cervix). Rate of Vaginal Delivery was 87.85% in cases of membrane stripping and 76% without membrane stripping. The complications were limited in the study group included: 1-Infection( 0.58%). 2-Bleeding 0.58%. 3-PROM 1.16%. It means that Membrane Stripping was somehow safe.


Artificial intelligence review:
Research summary
تتناول الدراسة التي أجريت في مشفى الأسد الجامعي باللاذقية بين 1/1/2013 و1/1/2014، تأثير تسليخ الأغشية الأمينوسية كطريقة غير دوائية لتحريض المخاض. شملت الدراسة 190 مريضة، منها 140 حالة تم فيها إجراء تسليخ الأغشية و50 حالة دون تسليخ. تم تقييم نضج عنق الرحم باستخدام مشعر بيشوب. أظهرت النتائج أن نسبة الاستجابة لتسليخ الأغشية بلغت 79.28% مقارنة بـ 54% في الحالات دون تسليخ. كانت نسبة الولادة الطبيعية 87.85% في حالات تسليخ الأغشية و76% دون تسليخ. كانت الاختلاطات محدودة وشملت الإنتان والنزف بنسبة 0.58% لكل منهما، وانبثاق الأغشية بنسبة 1.16%. تشير النتائج إلى أن تسليخ الأغشية كان إجراءً آمناً إلى حد ما وفعالاً في تحريض المخاض، خاصة في حالات عنق الرحم متوسط النضج.
Critical review
دراسة نقدية: تعتبر الدراسة مفيدة في تسليط الضوء على فعالية تسليخ الأغشية الأمينوسية كطريقة غير دوائية لتحريض المخاض. ومع ذلك، يمكن ملاحظة بعض النقاط التي قد تحتاج إلى تحسين. أولاً، كان من الأفضل تضمين مجموعة أكبر من المرضى لزيادة قوة النتائج. ثانياً، لم يتم التطرق بشكل كافٍ إلى الآثار الجانبية المحتملة على المدى الطويل لكل من الأم والجنين. ثالثاً، كان من الممكن تحسين الدراسة بمقارنة تسليخ الأغشية مع طرق أخرى لتحريض المخاض بشكل مباشر. وأخيراً، لم يتم التطرق إلى تأثير العوامل الاجتماعية والاقتصادية على نتائج الدراسة، مما قد يؤثر على تعميم النتائج.
Questions related to the research
  1. ما هي نسبة الاستجابة لتسليخ الأغشية في الدراسة؟

    نسبة الاستجابة لتسليخ الأغشية في الدراسة بلغت 79.28%.

  2. ما هي نسبة الولادة الطبيعية في حالات تسليخ الأغشية؟

    نسبة الولادة الطبيعية في حالات تسليخ الأغشية بلغت 87.85%.

  3. ما هي الاختلاطات التي تم رصدها في مجموعة الدراسة؟

    الاختلاطات شملت الإنتان والنزف بنسبة 0.58% لكل منهما، وانبثاق الأغشية بنسبة 1.16%.

  4. ما هي الفئة الأكثر استجابة لتسليخ الأغشية حسب مشعر بيشوب؟

    الفئة الأكثر استجابة لتسليخ الأغشية كانت حالات عنق الرحم متوسط النضج (درجة 4-5 حسب مشعر بيشوب).


References used
ELTORKEY,N;GRANT,GM.sweeping of membranes is an effective method of induction of labor in prolonged pregnancies . J-clinical obestetrics and gynecol . Canada . 2011 .P: 712-715
SWAN, RO. Induction of labor by membrane – stripping .clinical obestetrics and gynecol. USA . 2005 . P: 391-395
WEISSBERG , SM ; SPELLACY, W . Membrane – stripping to induce labor . JRepord Med . Netherland. 2009 .P: 81-89
rate research

Read More

For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this work, we present a technique that uses distant supervision in the form of span constraints (i.e. phrase bracketing) to improve performance in unsupervised constituency parsing. Using a relatively small number of span constraints we can substantially improve the output from DIORA, an already competitive unsupervised parsing system. Compared with full parse tree annotation, span constraints can be acquired with minimal effort, such as with a lexicon derived from Wikipedia, to find exact text matches. Our experiments show span constraints based on entities improves constituency parsing on English WSJ Penn Treebank by more than 5 F1. Furthermore, our method extends to any domain where span constraints are easily attainable, and as a case study we demonstrate its effectiveness by parsing biomedical text from the CRAFT dataset.
Personal identification based on handprint has been gaining more attention with the increasing needs of high level of security. In this study a novel approach for human recognition based on handprint is proposed. Wavelet transform was used to extra ct features presented in the palm image based on wavelet zero-crossing method. Firstly the wavelet transform of the whole palm image at the fourth level was worked out, which results in four matrices; three of them are detail matrices (i.e., horizontal, vertical and diagonal) as well as one approximation matrix. Throughout this study, only the detail matrices were used because the required information (i.e., hand lines and curves) is included in those matrices. Sixteen features were extracted from each detail matrix, and then arranged in one vector. Consequently, for each palm sample a feature vector consisting of 48 input features of the used neural network was obtained. For this purpose, a database consisting of 400 palm images belonging to 40 people at the rate of 10 images per person was built. Practical tests outcome showed that the designed system successfully indentified 91.36% of the tested images.
Considered inadequate maternity care during pregnancy and delivery largely responsible for the big annual losses for each of the parturient mothers and their newborns. So this study aimed to assess the midwives knowledge regarding the management of s econd stage of labor, and to find out the association between their knowledge and personal data during second stage of labor. This descriptive study was conducted in three hospitals at lattakia city, on a sample of 160 midwife. Results of the study showed that the level of midwives information to the second stage of labor was average, and the study of the impact of personal data on the level of midwives information about the second stage of labor show a statistically significant relationship between age and the level of Midwives Information, and between educational level and the level of their knowledge. The study also showed that There were no statistically significant differences between the experience and the level of information midwives relationship. The study recommends that; installing in-service educational program for Midwives to upgrade the techniques necessary to assess, evaluate and improve the quality of care rendered to laboring women, and to put an emphasis on conducting training course for the Midwives in order to change their malpractices and updating their knowledge with regular supervision on their performance.
Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models, those text representa tions are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model processes several documents at once and generates composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.
Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. To this end, we propose Label Reasoning Network(LRN), which sequentially reasons fine-grained entity labels by discovering and exploiting label dependencies knowledge entailed in the data. Specifically, LRN utilizes an auto-regressive network to conduct deductive reasoning and a bipartite attribute graph to conduct inductive reasoning between labels, which can effectively model, learn and reason complex label dependencies in a sequence-to-set, end-to-end manner. Experiments show that LRN achieves the state-of-the-art performance on standard ultra fine-grained entity typing benchmarks, and can also resolve the long tail label problem effectively.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا