Evidence map›Paper›PMID 26253582›Full record

ArticleInternational journal of computer assisted radiology and surgery2016

Procedural surgical skill assessment in laparoscopic training environments.

Munenori Uemura, Pierre Jannin, Makoto Yamashita, Morimasa Tomikawa, Tomohiko Akahoshi, Satoshi Obata, Ryota Souzaki, Satoshi Ieiri, Makoto Hashizume

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Article
  9. Objective assessment of laparoscopic targeting skills using a Short-Time Power of Difference (STPOD) method.International journal of computer assisted radiology and surgery · 2022
    Article
  10. Movement-level process modeling of microsurgical bimanual and unimanual tasks.International journal of computer assisted radiology and surgery · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons.Computational and mathematical methods in medicine · 2018
    Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Munenori UemuraDepartment of Advanced Medical Initiatives, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. munenori@dem.med.kyushu-u.ac.jp.
Pierre JanninINSERM, U1099, 35000, Rennes, France.
Makoto YamashitaDepartment of Advanced Medical Initiatives, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Morimasa TomikawaDepartment of Advanced Medicine and Innovative Technology, Kyushu University Hospital, Fukuoka, Japan.
Tomohiko AkahoshiDepartment of Advanced Medical Initiatives, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Satoshi ObataDepartment of Advanced Medicine and Innovative Technology, Kyushu University Hospital, Fukuoka, Japan.
Ryota SouzakiDepartment of Advanced Medicine and Innovative Technology, Kyushu University Hospital, Fukuoka, Japan.
Satoshi IeiriDepartment of Advanced Medicine and Innovative Technology, Kyushu University Hospital, Fukuoka, Japan.
Makoto HashizumeDepartment of Advanced Medical Initiatives, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to identify detailed differences in laparoscopic surgical processes between expert and novice surgeons in a training environment and demonstrate that surgical process modeling can be used for such detailed analysis.

methodsEleven expert surgeons each of whom had performed [Formula: see text] laparoscopic procedures were compared with 10 young surgeons each of whom had performed [Formula: see text] laparoscopic procedures, and five medical students. Each examinee performed a specific skill assessment task. During tasks, instrument motion was monitored using a video capture system. From the video, the corresponding workflow was recorded by labeling the surgeons' activities according to a predefined terminology. Activities represented manual work steps performed during the task, described by a combination of a verb (representing the action), a tool, and the involved structure. The results were described as the number of occurrences (times), average duration (seconds), total duration (seconds), minimal duration (seconds), maximal duration (seconds), and occupancy percentage (%).

resultsThe terminology for describing the processes of this task included 10 actions, six tools, four structures, and three events for each hand. There were 63 combinations of different possible activities; significant differences in 12 activities were observed between the expert and novice groups (young surgeons and medical students). The expert group performed the task with fewer occurrences and shorter duration than did the novice group in the left hand.

conclusionsWe identified differences in surgical process between experts and novices in laparoscopic surgical simulation. Our proposed method would be useful for education and training in laparoscopic surgery.

Indexed as

Clinical CompetenceEducation, Medical, GraduateHumansLaparoscopyOperative TimeTime FactorsLaparoscopic surgerySurgical process modelSurgical skillsSurgical training

Identifiers

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Read underepoch 390

Registered trials

None linked

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.