What is she best known for?
The fields of study she is best known for:
- Surgery
- Internal medicine
- Radiology
Her primary scientific interests are in Surgery, Laparoscopy, Nephrectomy, Endoscopy and Complication.
Her biological study spans a wide range of topics, including Renal cell carcinoma and Kidney disease.
Her biological study spans a wide range of topics, including Neck of urinary bladder, Benign disease, Perioperative, Suture and Retroperitoneal space.
Her studies deal with areas such as Stage, Carcinoma and Dissection as well as Nephrectomy.
Her Endoscopy study combines topics from a wide range of disciplines, such as Ureteroscopy and Lithotripsy.
Her research in Complication intersects with topics in Pneumothorax and Abdominal wall.
Her most cited work include:
- LAPAROSCOPIC VERSUS OPEN RADICAL NEPHRECTOMY: A 9-YEAR EXPERIENCE (511 citations)
- Prospective, randomized trial comparing shock wave lithotripsy and ureteroscopy for lower pole caliceal calculi 1 cm or less. (352 citations)
- Complications of Laparoscopic Nephrectomy in 185 Patients: A Multi-Institutional Review (285 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Surgery, Laparoscopy, Nephrectomy, Endoscopy and Ureter.
Her study in Surgery is interdisciplinary in nature, drawing from both Urinary system, Kidney and Kidney disease.
Her Urinary system study necessitates a more in-depth grasp of Internal medicine.
As a member of one scientific family, she mostly works in the field of Laparoscopy, focusing on Urology and, on occasion, Medical education.
Her work deals with themes such as Carcinoma, Renal cell carcinoma and Convalescence, which intersect with Nephrectomy.
Elspeth M. McDougall specializes in Ureter, namely Ureteroscopy.
She most often published in these fields:
- Surgery (77.33%)
- Laparoscopy (36.63%)
- Nephrectomy (25.00%)
What were the highlights of her more recent work (between 2009-2020)?
- Surgery (77.33%)
- Medical education (7.27%)
- Medical physics (7.56%)
In recent papers she was focusing on the following fields of study:
Elspeth M. McDougall mainly focuses on Surgery, Medical education, Medical physics, Urology and Curriculum.
Her Surgery research incorporates themes from Internal medicine, Nephrectomy and General surgery.
Elspeth M. McDougall has researched Medical education in several fields, including Surgical skills and Clinical competence.
Her research integrates issues of Laparoscopic surgery and Robotic surgery in her study of Medical physics.
Her Urothelium study, which is part of a larger body of work in Urology, is frequently linked to Technical skills, Residency training, Training level and Session, bridging the gap between disciplines.
Much of her study explores Laparoscopy relationship to Retrospective cohort study.
Between 2009 and 2020, her most popular works were:
- Best Practices for Robotic Surgery Training and Credentialing (107 citations)
- Validation Study of a Virtual Reality Robotic Simulator—Role as an Assessment Tool? (80 citations)
- Validation, Correlation, and Comparison of the da Vinci Trainer™ and the da Vinci Surgical Skills Simulator™ Using the Mimic™ Software for Urologic Robotic Surgical Education (69 citations)
In her most recent research, the most cited papers focused on:
- Surgery
- Internal medicine
- Radiology
Elspeth M. McDougall spends much of her time researching Surgery, Medical physics, Laparoscopy, Nephrectomy and Artificial intelligence.
Her Surgery study incorporates themes from Physical therapy, Laparoscopic nephrectomy and Virtual reality.
Elspeth M. McDougall has included themes like Motion, Concordance and Clinical competence in her Medical physics study.
Urinary system, General surgery and Renal surgery is closely connected to Endoscopy in her research, which is encompassed under the umbrella topic of Laparoscopy.
Her study in the field of Kidney surgery is also linked to topics like Tumor imaging.
Her work on Robotics, Robotic surgery and Robot as part of general Artificial intelligence research is frequently linked to Construct, bridging the gap between disciplines.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
