Introduction of a learning model for type 1 loop excision of the transformation zone of the uterine cervix in undergraduate medical students: a prospective cohort study

Arch Gynecol Obstet. 2019 Mar;299(3):817-824. doi: 10.1007/s00404-018-5019-7. Epub 2019 Jan 4.

Abstract

Purpose: We address the impact of applying loop electrosurgical excision procedure (LEEP) under direct colposcopic vision teaching to our undergraduates using a self-developed simulation model and a standardized assessment to evaluate the progress of learning.

Methods: The undergraduate teaching module was composed of a theoretical course on cervical dysplasia, colposcopy, electrosurgery and excisional procedures of the uterine cervix. This was followed by hands-on practical rounds. During the hands-on practice the students performed five "type 1" LEEP under direct colposcopic vision on the self-developed simulator. Based on specimen fragmentation and excision accuracy a score system was established. The students were asked to answer a course evaluation questionnaire.

Results: The accuracy of the excisions showed a statistically significant improvement during the five training procedures (excision depth 7.34 ± 1.60-8.54 ± 1.67 mm, p = 0.0041; deviation from target cone thickness 0.88 ± 1.16-0.13 ± 0.94 mm, p = 0.0116). The fragmentation of the conus decreased (2.57 ± 1.26-1.29 ± 0.60 pieces, p < 0.0001). All this led to a general improvement of the LEEP score (2.59 ± 1.93-0.84 ± 1.03, p = 0.001). The student's questionnaire revealed a subjective satisfaction and improvement of their knowledge in pathomechanism, diagnosis and therapy of cervical pathologies.

Conclusion: Undergraduate surgical training, in cervical excisional procedure, is a successful method in improving the students' perception and management of cervical pathologies.

Keywords: Colposcopy; Cone biopsy; LEEP; LLETZ; Simulation training; Teaching.

MeSH terms

  • Adult
  • Cervix Uteri / surgery*
  • Colposcopy / methods*
  • Female
  • Humans
  • Prospective Studies
  • Students, Medical / statistics & numerical data*