Dokumentdetails
ID
doi:10.1007/s00192-023-05591-5...
Autor
Mikos, Themistoklis Theodoulidis, Iakovos Kioussis, George Karalis, Tilemachos Papaioannou, Sofia Grimbizis, Grigoris F.Langue
enEditor
Springer
Kategorie
Urology
Jahr
2023
Auflistungsdatum
12.07.2023
Schlüsselwörter
sacrocolpopexy mesh erosion mesh excision cuff meshoma vaginoscopy vaginal-endoscopic approach scp vaginal-endoscopic sinus meshZusammenfassung
Introduction and hypothesis The objective was to present endoscopic images of a meshoma and describe the complete excision of a complicated mesh after sacrocolpopexy (SCP) using a combined vaginal-endoscopic technique.
Methods We present a video documentation of an innovative technique.
A 58-year-old woman was referred with painless, foul-smelling vaginal discharge and recurrent vaginal mesh erosions.
She had undergone a laparoscopic SCP 12 years ago and her symptoms had begun 5 years ago.
A pre-operative MRI scan revealed a cuff meshoma and an inflammatory sinus around the mesh extending from the cuff to the sacral promontory.
Under general anesthesia, a 30° hysteroscope was inserted transvaginally into the sinus, where the retained mesh was seen in the form of a shrunken meshoma, and then the mesh arms were recognized extending cephalad into a sinus tract.
Under direct endoscopic visualization, the mesh was carefully mobilized at its highest point with the use of laparoscopic grasping forceps.
Then, the mesh was dissected with hysteroscopic scissors in close proximity to the bone.
No peri-operative complications were recognized.
Results A combined vaginal-endoscopic approach was successfully used to remove an eroded mesh and cuff meshoma after SCP.
Conclusion This procedure offers a minimally invasive, low-morbidity, and rapid-recovery approach.
Mikos, Themistoklis,Theodoulidis, Iakovos,Kioussis, George,Karalis, Tilemachos,Papaioannou, Sofia,Grimbizis, Grigoris F., 2023, Cuff meshoma post-laparoscopic sacrocolpopexy: vaginal-endoscopic mesh excision, Springer
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
methods salience efficiency quantization language slim-llm llms
Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques
using data ml prediction 0 suicide adolescents
