Evidence map›Paper›PMID 36991855›Full record

ArticleSensors (Basel, Switzerland)2023

Motion Smoothness-Based Assessment of Surgical Expertise: The Importance of Selecting Proper Metrics.

Farzad Aghazadeh, Bin Zheng, Mahdi Tavakoli, Hossein Rouhani

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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

11 citing papers in PubMed.

  1. Performance using a visuo-haptic surgical simulator is affected by age.Advances in simulation (London, England) · 2026
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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

4 authors.

Farzad AghazadehDepartment of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.ORCID 0000-0002-4377-7595
Bin ZhengDepartment of Surgery, University of Alberta, Edmonton, AB T6G 2B7, Canada.ORCID 0000-0003-3476-5936
Mahdi TavakoliDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada.ORCID 0000-0002-7427-6961
Hossein RouhaniDepartment of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.ORCID 0000-0002-4790-9313

Funding

Alberta Minister of Jobs, Economy and Innovation, Major Innovation Fund - Autonomous Systems Initiative MIF01 T4 P1
6 · The paper itself

Abstract

The smooth movement of hand/surgical instruments is considered an indicator of skilled, coordinated surgical performance. Jerky surgical instrument movements or hand tremors can cause unwanted damages to the surgical site. Different methods have been used in previous studies for assessing motion smoothness, causing conflicting results regarding the comparison among surgical skill levels. We recruited four attending surgeons, five surgical residents, and nine novices. The participants conducted three simulated laparoscopic tasks, including peg transfer, bimanual peg transfer, and rubber band translocation. Tooltip motion smoothness was computed using the mean tooltip motion jerk, logarithmic dimensionless tooltip motion jerk, and 95% tooltip motion frequency (originally proposed in this study) to evaluate their capability of surgical skill level differentiation. The results revealed that logarithmic dimensionless motion jerk and 95% motion frequency were capable of distinguishing skill levels, indicated by smoother tooltip movements observed in high compared to low skill levels. Contrarily, mean motion jerk was not able to distinguish the skill levels. Additionally, 95% motion frequency was less affected by the measurement noise since it did not require the calculation of motion jerk, and 95% motion frequency and logarithmic dimensionless motion jerk yielded a better motion smoothness assessment outcome in distinguishing skill levels than mean motion jerk.

Indexed as

LaparoscopySurgeonsHandHumansMotionMovementSurgical Instrumentsminimally invasive surgerymotion jerkmotion trackingmovement smoothnesssurgical skill assessment

Identifiers

PMID36991855
PMCPMC10057623

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Registered trials

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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.