The importance of clinically and ethically fine-tuning decision-making about cesarean delivery

2017 ◽  
Vol 45 (5) ◽  
Author(s):  
Michelle T. Nguyen ◽  
Laurence B. McCullough ◽  
Frank A. Chervenak

AbstractIn obstetric practice, each pregnant woman presents with a composite of maternal and fetal characteristics that can alter the risk of significant harm without cesarean intervention. The hospital’s availability of resources and the obstetrician’s training, experience, and skill level can also alter the risk of significant harm without cesarean intervention. This paper proposes a clinical ethical framework that takes these clinical and organizational factors into account, to promote a deliberative rather than simplistic approach to decision-making and counseling about cesarean delivery. The result is a clinical ethical framework that should guide the obstetrician in fine-tuning his or her evidence-based, beneficence-based analysis of specific clinical and organizational factors that can affect the strength of the beneficence-based clinical judgment about cesarean delivery. We illustrate the clinical application of this framework for three common obstetric conditions: Category II fetal heart rate tracing, prior non-classical cesarean delivery, and breech presentation.

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Michelle T. Nguyen ◽  
Laurence B. McCullough ◽  
Frank A. Chervenak ◽  
Kathryn J. Shaw ◽  
Dominique Luckey

Abstract Background A fetal diagnosis poses ethical challenges when a woman requests elective cesarean delivery for psychosocial reasons. We address the ethical challenges of counseling such patients. Case presentation A 36-year-old G4P2012 has chosen to continue a pregnancy despite a high likelihood of trisomy 18. At 36.5 weeks she was admitted for preeclampsia with severe features and requested to be delivered by primary cesarean section. Due to the poor prognosis associated with trisomy 18, the patient’s request for cesarean delivery was declined even when her baby changed to breech presentation with Category 2 fetal heart rate (FHT). The patient subsequently experienced a traumatic stillbirth and post-traumatic shock disorder (PTSD). Conclusion The obstetrician’s goal should be to transform the patient’s request into an informed decision. The obstetrician should explain that, while a cesarean could increase the likelihood of a live birth, it will not alter long-term neonatal outcomes and entails net biomedical risk for the current and future pregnancies. The obstetrician should ensure that the patient understands these clinical realities. The obstetrician should support the patient’s decision-making about whether to accept the risks of cesarean delivery for psychosocial benefit. The obstetrician should initiate counseling during prenatal visits to empower the patient with information to meaningfully exercise her autonomy. If the patient makes an informed decision for cesarean delivery, it becomes ethically permissible. Plans regarding intrapartum management and mode of delivery should be documented in case the patient is delivered by a physician who was not involved in prenatal counseling.


2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.


1999 ◽  
Vol 38 (04/05) ◽  
pp. 279-286 ◽  
Author(s):  
L. L. Weed

AbstractIt is widely recognised that accessing and processing medical information in libraries and patient records is a burden beyond the capacities of the physician’s unaided mind in the conditions of medical practice. Physicians are quite capable of tremendous intellectual feats but cannot possibly do it all. The way ahead requires the development of a framework in which the brilliant pieces of understanding are routinely assembled into a working unit of social machinery that is coherent and as error free as possible – a challenge in which we ourselves are among the working parts to be organized and brought under control.Such a framework of intellectual rigor and discipline in the practice of medicine can only be achieved if knowledge is embedded in tools; the system requiring the routine use of those tools in all decision making by both providers and patients.


Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1052
Author(s):  
Leang Sim Nguon ◽  
Kangwon Seo ◽  
Jung-Hyun Lim ◽  
Tae-Jun Song ◽  
Sung-Hyun Cho ◽  
...  

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.


Author(s):  
Emma J. Qureshey ◽  
Hector Mendez-Figueroa ◽  
Rachel L. Wiley ◽  
Asha B. Bhalwal ◽  
Suneet P. Chauhan

2004 ◽  
Vol 190 (3) ◽  
pp. 763-768 ◽  
Author(s):  
Yannik Vézina ◽  
Emmanuel Bujold ◽  
Jocelyne Varin ◽  
Gérald P Marquette ◽  
Marc Boucher

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