Evidence map›Paper›PMID 35145990›Full record

ArticleFrontiers in surgery2021

Surgical Process Modeling for Open Spinal Surgeries.

Fabio Carrillo, Hooman Esfandiari, Sandro Müller, Marco von Atzigen, Aidana Massalimova, Daniel Suter, Christoph J Laux, José M Spirig, Mazda Farshad, Philipp Fürnstahl

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Frontiers in surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05993845 (Modeling and Workflow Recognition for the Anterior Approach in Total Hip Arthroplasty), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.3field-weighted citation impact, top 22% of its field
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.

NCT05993845 recruitingnot on this mapstarted 2024, after this paper: background citation

Modeling and Workflow Recognition for the Anterior Approach in Total Hip Arthroplasty

TypeobservationalSponsorPhilipp FürnstahlRan2024 to 2026Enrolled60ConditionsHip ProsthesisArmsRecording of total hip arthroplasty
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. 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

10 authors at 2 institutions in 1 country.

Fabio CarrilloResearch in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Hooman EsfandiariResearch in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Sandro MüllerResearch in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Marco von AtzigenResearch in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Aidana MassalimovaResearch in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Daniel SuterResearch in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Christoph J LauxDepartment of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
José M SpirigDepartment of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Mazda FarshadDepartment of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Philipp FürnstahlResearch in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
University of Zurich · CHUniversitätsklinik Balgrist · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modern operating rooms are becoming increasingly advanced thanks to the emerging medical technologies and cutting-edge surgical techniques. Current surgeries are transitioning into complex processes that involve information and actions from multiple resources. When designing context-aware medical technologies for a given intervention, it is of utmost importance to have a deep understanding of the underlying surgical process. This is essential to develop technologies that can correctly address the clinical needs and can adapt to the existing workflow. Surgical Process Modeling (SPM) is a relatively recent discipline that focuses on achieving a profound understanding of the surgical workflow and providing a model that explains the elements of a given surgery as well as their sequence and hierarchy, both in quantitative and qualitative manner. To date, a significant body of work has been dedicated to the development of comprehensive SPMs for minimally invasive baroscopic and endoscopic surgeries, while such models are missing for open spinal surgeries. In this paper, we provide SPMs common open spinal interventions in orthopedics. Direct video observations of surgeries conducted in our institution were used to derive temporal and transitional information about the surgical activities. This information was later used to develop detailed SPMs that modeled different primary surgical steps and highlighted the frequency of transitions between the surgical activities made within each step. Given the recent emersion of advanced techniques that are tailored to open spinal surgeries (e.g., artificial intelligence methods for intraoperative guidance and navigation), we believe that the SPMs provided in this study can serve as the basis for further advancement of next-generation algorithms dedicated to open spinal interventions that require a profound understanding of the surgical workflow (e.g., automatic surgical activity recognition and surgical skill evaluation). Furthermore, the models provided in this study can potentially benefit the clinical community through standardization of the surgery, which is essential for surgical training.

Indexed as

bottom-up modelingopen spinal surgerypedicle screwspinal fusionspinal instrumentationsurgical process modelingtop-down modeling

Identifiers

PMID35145990
PMCPMC8821818
OpenAlexW4206906713

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.