ArticleJournal of the Turkish German Gynecological Association2020
Significant improvement of laparoscopic knotting time in medical students through manual training with potential cost savings in laparoscopy - an observational study
Article in Journal of the Turkish German Gynecological Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Evaluation of the concept of students learning practical gynecological and obstetric skills at a large German university hospital, using the example of the Homburg university women's clinic.Archives of gynecology and obstetrics · 2026Article
- Training of young medical professionals: implementation of modern training concepts, taking into account the changed framework conditions for training-a pilot project for operational subjects.Archives of gynecology and obstetrics · 2024Article
- The Future in Standards of Care for Gynecologic Laparoscopic Surgery to Improve Training and Education.Journal of clinical medicine · 2022Review
- Students' attitudes toward digital learning during the COVID-19 pandemic: a survey conducted following an online course in gynecology and obstetrics.Archives of gynecology and obstetrics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Laparoscopy is a standard procedure in operative gynaecology, but laparoscopic simulator training for novices/junior surgeons is not currently well-established. The aims of this study were to demonstrate that a laparoscopic knot course for trainees can significantly shorten the knotting time and to perform a counter-value calculation for the clinic’s costs. Material and Methods: An observational study was performed with exercises on a laparoscopic box trainer as part of the practical clerkship in gynaecology and obstetrics between 07.10.2019-31.01.2020. At the beginning and at the end of the exercises, the participants made a laparoscopic knot and the difference in knotting time, Δt in seconds (s) was measured. Results: Eighty-eight medical students needed an average of 247.1 s for the first laparoscopic knot at the beginning of the course and an average of 45.43 s for the second at the end of the course. Mean shortening of the knotting time was 201.67 s or 81.6% (p=0.02). Calculating costs of an average of €40-50 for an operation minute would mean a cost saving of at least €120-150 for a partial node. Conclusion: Trainees can significantly improve their operative skills in a short time with the aid of surgical simulation training. Such training can be beneficial for clinics by reducing the operating time if the basics, such as sewing and instrument guidance, are learned on a simulator. We therefore suggest that operative simulation training should be mandatory in medical education.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.