Evidence map›Paper›PMID 40032795›Full record

ArticleSpine deformity2025

Building consensus: development of a best practice guideline (BPG) for avoiding errors in robotic-assisted spine surgery (RASS).

Michael G Vitale, Ritt R Givens, 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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In one paragraph

Article in Spine deformity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

18 authors.

Michael G VitaleDivision of Pediatric Orthopedics, Columbia University Medical Center, New York, NY, USA.
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
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 SethiDepartment of Health Systems and Population Health, Center for Neurosciences and Spine, Virginia Mason Medical Center, 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.
Terrence T KimDepartment of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
David SkaggsDepartment of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionWith the rapid increase in the use of robotic-assisted spine surgery (RASS), reports describing complications have inevitably emerged. This study builds on previous work done to identify, characterize, and classify potential sources of error in spine surgery performed with enabling technology in the operating room. The goal of this study is to leverage expert opinion to develop a set of best practice guidelines that can be employed to minimize complications and optimize patient safety, specifically as it relates to RASS.

methodsAfter assembling a group of attending spine surgeons experienced in the use of RASS across the country, formal consensus regarding the best practices was developed using the Delphi method and nominal group technique. After a review of the relevant literature and evidence, an initial survey of study group members (n=12) helped frame potential areas for investigation. Statements were subsequently edited, removed, or elaborated upon during four iterative rounds of live discussion with the opportunity for panelists to propose new guidelines at any point in the process. Respondents were able to suggest modifications and refine the statements until consensus, defined as ≥ 80% agreement, was achieved.

resultsAfter a three-round iterative survey and video conference Delphi process, followed by an in-person meeting at the Summit for Safety in Spine Surgery, consensus was achieved on 27 best practice guideline statements. This BPG had the key focus areas of 1) general protocols, 2) screw planning/execution, 3) optimization of surgical technique, and 4) areas for robotic improvement. (available at https://safetyinspinesurgery.com/ ).

conclusionThis work provides expert insight into the best practices for minimizing errors in RASS with the presentation of 27 recommendations that can serve to reduce practice variability, optimize safety, and guide future research.

Indexed as

Medical ErrorsPractice Guidelines as TopicRobotic Surgical ProceduresSpineConsensusDelphi TechniqueHumansPatient SafetyBest practice guidelineDelphi processRobotic-assisted spine surgerySafety in spine surgery

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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.