Evidence map›Paper›PMID 27928669›Full record

ArticleSurgical endoscopy2017

Analysis of motion in laparoscopy: the deconstruction of an intra-corporeal suturing task.

Monica A Farcas, Maeve O'Neill Trudeau, Ahmed Nasr, J Ted Gerstle, Brian Carrillo, Georges Azzie

Registry-linked trialAbstract read
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04646304 (Motion Capture as a Tool for the Assessment of Laparoscopic Performance), which is not on this map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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.

NCT04646304 nacompletednot on this mapstarted 2021, after this paper: background citation

Motion Capture as a Tool for the Assessment of Laparoscopic Performance

TypeinterventionalSponsorOttawa Hospital Research InstituteRan2021 to 2024Enrolled26ConditionsHealthy, Adult ALLArmsObjective Feedback (Motion Capture)
3 · Its place in the literature

Who cites it

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

  1. Pooled it
  2. Article
  3. Article
  4. Development of a synchronous motion-tracking and video capture tool for flexible ureteroscopy.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2024
    Article
  5. Article
  6. Article
  7. Application of a Perception NeuronJournal of clinical medicine · 2019
    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

6 authors.

Monica A FarcasDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Maeve O'Neill TrudeauDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Ahmed NasrDivision of Pediatric Surgery, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada.
J Ted GerstleDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Brian CarrilloDivision of General and Thoracic Surgery, Center for Image Guided Innovation and Therapeutic Intervention, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
Georges AzzieDepartment of Surgery, University of Toronto, Toronto, ON, Canada. georges.azzie@sickkids.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study analyzes instrument motion for segments of a defined intra-corporeal suturing task in a laparoscopic simulator. We describe a system providing real-time velocity and acceleration assessment in the performance of this task. Analysis of the deconstructed task segments allows targeted assessment and teaching.

methodsA traditional box trainer was fitted with a custom-built motion-tracking system. Participants were stratified into novice, intermediate and expert groups. They performed a defined intra-corporeal suturing task. Real-time data were collected in four degrees of freedom (DOFs) (Roll, Surge, Pitch, Yaw). The task was then deconstructed into four segments: loading needle/pull-through, double-throw knot, first single-throw knot, and second single-throw knot. Motion analysis parameters (MAPs) were studied for each DOF.

resultsSixty-four participants were tested (14 novices, 19 intermediates, 31 experts). The largest difference in MAPs was seen in the 'double-throw knot' segment. MAPs for the 'loading needle/pull-through' segment revealed differences between novices and experts in Roll and Pitch DOFs only. For the 'first single knot' segment, similar MAP trends were noted across all DOFs, with significant differences between novices versus experts and intermediates versus experts. For the 'second single knot' segment, the difference in MAPs was preserved only for novices versus experts.

conclusionsBy analyzing motion for a defined suturing task in a laparoscopic simulator, we can gain insight into the specific hand motions distinguishing experts from non-experts. Such information may allow teaching in a more focused, effective and efficient manner.

Indexed as

Clinical CompetenceLaparoscopyMotionSuture TechniquesAccelerationHandHumansNeedlesSimulation TrainingSuturesTask Performance and AnalysisIntra-corporeal suturingLaparoscopyMotion analysis tracking

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.