Evidence map›Paper›PMID 42141188›Full record

ArticleSurgical endoscopy2026

Standardization of surgical gesture taxonomy: a SAGES Delphi consensus study.

Maria Clara Morais, Aditya Amit Godbole, Emaad Iqbal, Mattia Ballo, Anthony Jarc, Beatrice Van Amsterdam, Brent Matthews, Christopher M Schlachta, Daniel A Donoho, Daniel A Hashimoto and 21 more

Abstract read
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Article in Surgical endoscopy, 2026. 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
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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

31 authors.

Maria Clara MoraisIntraoperative Performance Analytics Laboratory, Department of Surgery, Northwell Health, New York, NY, USA. drmariamorais@gmail.com.ORCID http://orcid.org/0000-0003-3752-584X
Aditya Amit GodboleIntraoperative Performance Analytics Laboratory, Department of Surgery, Northwell Health, New York, NY, USA.
Emaad IqbalIntraoperative Performance Analytics Laboratory, Department of Surgery, Northwell Health, New York, NY, USA.
Mattia BalloDepartment of Surgical, Oncological and Gastroenterological Sciences, University of Padua, ⁠⁠Padua, Italy.
Anthony JarcIntuitive AI and Digital, Sunnyvale, CA, USA.
Beatrice Van AmsterdamMedtronic Digital Technologies, London, UK.
Brent MatthewsDepartment of Surgery, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Christopher M SchlachtaDepartment of Surgery, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Daniel A DonohoChildren's National Hospital, Washington, DC, USA.
Daniel A HashimotoDepartment of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Jay A RedanDepartment of Surgery, Advent Health-Celebration, University of Central Florida College of Medicine, Celebration, FL, USA.
Jayson MarwahaDepartment of Surgery, University of Michigan, Ann Arbor, MI, USA.
Jon GouldDepartment of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA.
Liane S FeldmanDepartment of Surgery, McGill University, Montreal, QC, Canada.
Ozanan MeirelesDepartment of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Pieter De BackerOrsi Academy and Department of Human Structure and Repair, Ghent University, Ghent, Belgium.
Pietro MascagniBioimage Analysis Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Simon LaPlanteDepartment of Surgery, Mayo Clinic, Rochester, MN, USA.
Ankit SarinDepartment of Surgery, University of CA - Davis, Davis, CA, USA.
Anastasiya ShchatskoPiedmont Henry Hospital, Stockbridge, GA, USA.
Danielle WalshDepartment of Surgery, University of Kentucky, Lexington, KY, USA.
Danyal M FerDepartment of Surgery, David Grant Medical Center, Travis Air Force Base, Fairfield, CA, USA.
David Romero FunesDepartment of Surgery, Cleveland Clinic Florida, Weston, FL, USA.
Kimimasa SasakiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Tokyo, Japan.
Nova SzokaDepartment of Surgery, West Virginia University, Morgantown, WV, USA.
Sara S LazzarettiProfessional Affairs, Asensus Surgical, Milan, Italy.
Sharona B RossDepartment of Surgery, Advent Health-Tampa, University of Central Florida College of Medicine, Tampa, FL, USA.
Thomas SchnelldorferSurgical Imaging Lab, Tufts Medical Center, Boston, MA, USA.
Axel KriegerDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Andrew J HungDepartment of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Filippo FilicoriIntraoperative Performance Analytics Laboratory, Department of Surgery, Northwell Health, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial intelligence (AI) for surgical workflow analysis often fails to generalize because surgical actions lack a standardized, fine-grained representation. Gesture-level "tokenization" of surgery, capturing instrument-tissue interactions as the smallest intentional functional units, offers greater technical specificity than phase- or step-level labels and has demonstrated associations with proficiency and clinical outcomes. However, the field remains fragmented by heterogeneous gesture terminology, limiting dataset interoperability and model reproducibility.

methodsWe conducted a SAGES-led, accelerated Delphi consensus process to establish a standardized surgical gesture taxonomy. Starting with 270 literature-derived gesture terms, we employed a novel hybrid pipeline combining large language model (LLM)-assisted semantic clustering with multi-round expert review. The process involved two Delphi surveys (open-ended, then structured agreement) with a predefined ≥ 80% agreement threshold, a pilot interactive video-based validation task where participants labeled 30 surgical clips, and a final in-person consensus meeting with live anonymous polling.

resultsAcross iterative refinement, the taxonomy evolved from 106 gestures in 11 clusters to a hierarchical framework of Clusters, Gestures, and Sub-gestures, which, after consolidation and pilot annotation, reached a final consensus taxonomy comprising 10 clusters, 24 gestures, and 46 sub-gestures. The panel rejected dominant-instrument-only labeling, supporting multi-instrument annotation to capture assisting actions critical to surgical quality. Video-based validation demonstrated high agreement for multiple gestures (e.g., coagulate, suction, irrigate, staple, clip, needle drive), while identifying predictable ambiguities among semantically proximate actions (e.g., cut vs seal; grasp vs clamp; dissect vs spread), informing final revisions.

conclusionThis work establishes a standardized, hierarchical taxonomy for surgical gestures, providing a foundational language for surgical data science. This framework is designed to reduce annotation variability, enable reliable cross-study comparisons, and accelerate the development of scalable video-based assessment, computer vision, and autonomous systems. Defining temporal boundaries for these gestures was identified as the next critical step.

Indexed as

Artificial IntelligenceGesturesConsensusDelphi TechniqueHumansTerminology as TopicDelphi consensusMinimally invasive surgerySurgical gestures

Identifiers

PMID42141188

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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.