J
Jayne Fountain
Researcher at University of Leeds
Publications - 5
Citations - 1054
Jayne Fountain is an academic researcher from University of Leeds. The author has contributed to research in topics: Hysterectomy & Quality of life. The author has an hindex of 4, co-authored 5 publications receiving 1003 citations.
Papers
More filters
Journal ArticleDOI
The eVALuate study: two parallel randomised trials, one comparing laparoscopic with abdominal hysterectomy, the other comparing laparoscopic with vaginal hysterectomy.
Ray Garry,Jayne Fountain,Su Mason,Jeremy Hawe,Vicky Napp,Jason Abbott,Richard Clayton,Graham Phillips,Mark Whittaker,Richard J. Lilford,Stephen Bridgman,Julia Brown +11 more
TL;DR: The trial comparing vaginal hysterectomy with laparoscopic hystèrectomy was underpowered and is inconclusive on the rate of major complications; however, vaginal HystereCTomy took less time.
Journal ArticleDOI
EVALUATE hysterectomy trial: a multicentre randomised trial comparing abdominal, vaginal and laparoscopic methods of hysterectomy.
Raymond Garry,Jayne Fountain,Julia Brown,Andrea Manca,Su Mason,Mark Sculpher,Vicky Napp,Stephen Bridgman,Joanne Gray,Richard J. Lilford +9 more
TL;DR: ALH is associated with a significantly higher risk of major complications and takes longer to perform than AH, but is, however, associated with less pain, quicker recovery and better short-term QoL after surgery than AH.
Journal ArticleDOI
Cost effectiveness analysis of laparoscopic hysterectomy compared with standard hysterectomy: results from a randomised trial
TL;DR: Laparoscopic hysterectomy is not cost effective relative to vaginal hystEREctomy, and its cost effectiveness relative to the abdominal procedure is finely balanced.
Journal ArticleDOI
Psychometric properties of the Body Image Scale in women with benign gynaecological conditions
TL;DR: The Body Image Scale was shown to be a reliable and valid tool for assessing body image in women with benign gynaecological conditions and for use in clinical trials involving such women.
Journal ArticleDOI
A practical approach to a multi‐level analysis with a sparse binary outcome within a large surgical trial
TL;DR: In this paper, a linear mixed logistic regression model with logit link function was used to model the probability of a major complication, with surgeon fitted as a random effect, and the model was fitted using the method of maximum likelihood in SAS.