Evidence map›Paper›PMID 40167985›Full record

ArticleSpine deformity2025

Development of a classification system for potential sources of error in robotic-assisted spine surgery.

Ritt R Givens, Terrence T Kim, Matan S Malka, Kevin Lu, Thomas M Zervos, Joseph Lombardi, Zeeshan Sardar, Ronald Lehman, Lawrence Lenke, Rajiv Sethi and 8 more

Abstract read
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Article in Spine deformity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

18 authors.

Ritt R GivensDivision of Pediatric Orthopedics, Columbia University Medical Center, New York, NY, USA. rrg2158@cumc.columbia.edu.ORCID http://orcid.org/0009-0005-1641-9855
Terrence T KimDepartment of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Matan S MalkaDivision of Pediatric Orthopedics, Columbia University Medical Center, New York, NY, USA.
Kevin LuDivision of Pediatric Orthopedics, Columbia University Medical Center, New York, NY, USA.
Thomas M ZervosDivision of Pediatric Orthopedics, Columbia University Medical Center, New York, NY, USA.
Joseph LombardiDepartment of Orthopedic Surgery, Columbia University, New York, NY, USA.
Zeeshan SardarDepartment of Orthopedic Surgery, Columbia University, New York, NY, USA.
Ronald LehmanDepartment of Orthopedic Surgery, Columbia University, New York, NY, USA.
Lawrence LenkeDepartment of Orthopedic Surgery, Columbia University, New York, NY, USA.
Rajiv SethiCenter for Neurosciences and Spine, Virginia Mason Medical Center, Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
Stephen LewisUniversity Health Network, Toronto Western Hospital, Toronto, ON, Canada.
Daniel HedequistBoston Children's Hospital/Harvard Medical School, Boston, MA, USA.
Themistocles ProtopsaltisDepartment of Orthopaedic Surgery, NYU Grossman School of Medicine, NYU Langone Health, New York, NY, USA.
A Noelle LarsonDepartment of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.
Sheeraz QureshiDepartment of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA.
Brandon CarlsonMarc a. Asher MD Comprehensive Spine Center, University of Kansas Medical Center, Kansas City, KS, USA.
David SkaggsDepartment of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Michael G VitaleDivision of Pediatric Orthopedics, Columbia University Medical Center, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeRobotic-assisted spine surgery (RASS) has increased in prevalence over recent years, and while much work has been done to analyze differences in outcomes when compared to the freehand technique, little has been done to characterize the potential pitfalls associated with using robotics. This study's goal was to leverage expert opinion to develop a classification system of potential sources of error that may be encountered when using robotics in spine surgery. This not only provides practitioners, particularly those in the early stages of robotic adoption, with insight into possible sources of error but also provides the community at large with a more standardized language through which to communicate.

methodsThe Delphi method, which is a validated system of developing consensus, was utilized. The method employed an iterative presentation of classification categories that were then edited, removed, or elaborated upon during several rounds of discussion. Voting took place to accept or reject the individual classification categories with consensus defined as ≥ 80% agreement.

resultsAfter a three-round iterative survey and video conference Delphi process, followed by an in-person meeting at the Safety in Spine Surgery Summit, consensus was achieved on a classification system that includes four key types of potential sources of error in RASS as well as a list of the most commonly identified sources within each category. Initial sources of error that were considered included: cannula skidding/skive, penetration, screw misplacement, registration failure, and frame shift. After completion of the Delphi process, the final classification included four major types of pitfalls including: Reference/Navigation, Patient Factors, Technique, and Equipment Factors (available at https://safetyinspinesurgery.com/ ).

conclusionThis work provides expert insight into potential sources of error in the setting of robotic spine surgery. The working group established four discrete categories while providing a standardized language to unify communication.

Indexed as

Medical ErrorsRobotic Surgical ProceduresSpineConsensusDelphi TechniqueHumansDelphi processError ClassificationRobotic-assisted spine surgerySafety in spine surgery

Identifiers

PMID40167985

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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.