Evidence map›Paper›PMID 41664835›Full record

ArticleThe British journal of surgery2026

Development of a stakeholder-informed framework for the implementation of surgical sabermetrics to enhance training and education.

Lachlan Dick, Emma Howie, Joel Norton, Connor Boyle, Andrew Merriman, Victoria Ruth Tallentire, Roger D Dias, Douglas S Smink, Richard J E Skipworth, Steven Yule

Abstract read
In one paragraph

Article in The British journal of surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  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.

Lachlan DickSurgical Sabermetrics Laboratory, Usher Institute, University of Edinburgh, Edinburgh, UK.ORCID 0000-0002-4341-9501
Emma HowieSurgical Sabermetrics Laboratory, Usher Institute, University of Edinburgh, Edinburgh, UK.
Joel NortonSurgical Sabermetrics Laboratory, Usher Institute, University of Edinburgh, Edinburgh, UK.
Connor BoyleSurgical Sabermetrics Laboratory, Usher Institute, University of Edinburgh, Edinburgh, UK.ORCID 0000-0003-2124-607X
Andrew MerrimanMedical Education Directorate, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK.
Victoria Ruth TallentireMedical Education Directorate, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK.
Roger D DiasMedical AI & Cognitive Engineering (MAICE) Lab/STRATUS Center for Medical Simulation, Department of Emergency Medicine, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, USA.
Douglas S SminkDepartment of Surgery, Brigham and Women's Hospital/Harvard Medical School, Boston, Massachusetts, USA.
Richard J E SkipworthSurgical Sabermetrics Laboratory, Usher Institute, University of Edinburgh, Edinburgh, UK.ORCID 0000-0003-1428-4078
Steven YuleSurgical Sabermetrics Laboratory, Usher Institute, University of Edinburgh, Edinburgh, UK.ORCID 0000-0001-9889-9090

Funding

HS Lothian Medical Education DirectorateMedical Education FellowshipNHS Research Scotland (NRS) via a clinician post
6 · The paper itself

Abstract

backgroundSurgical training relies heavily on subjective performance evaluation, which is resource-intensive and prone to assessor bias. Advances in digital surgery offer opportunities for objective assessment. While validity evidence for data-driven assessments increases, strategies for implementation in surgical training remain scarce. The aim of this study was to leverage stakeholder insights to develop an implementation framework for integrating data-driven surgical sabermetrics into training curricula.

methodsStructured workshops were conducted at two international surgical conferences (the Association of Surgeons of Great Britain and Ireland Congress, Edinburgh, May 2025 and the International Conference on Surgical Education and Training, Edinburgh, June 2025). Delegates participated in facilitated discussions, interactive polling, and group concept-mapping exercises to explore opportunities, delivery modalities, access rights, and contextualization for surgical performance metrics. Stakeholder perceptions were used to iteratively develop an implementation framework, balancing applicability to current training pathways and capturing the nuances of data-driven insights.

resultsA total of 54 surgical trainees and trainers from 13 countries contributed. Opportunities centred on enhancing objective feedback, assessing non-technical skills, and tracking trainee progression. Video-based delivery and real-time feedback were prioritized for technical skills, dashboards were prioritized for non-technical and cognitive skills, and structured reports were prioritized for performance-based metrics. Supervising surgeons and training leads were identified as essential users of trainee data, with integration of multimodal data (for example surgeon physiology, case complexity) deemed essential for contextualization.

conclusionThis study presents an implementation framework for surgical sabermetrics in training. The framework provides practical guidance on delivery, access, and integration of performance metrics, supporting data-driven feedback to optimize trainee development, advance surgical education, and improve patient outcomes.

Indexed as

Clinical CompetenceEducation, Medical, GraduateGeneral SurgeryCurriculumHumansStakeholder ParticipationUnited Kingdom

Identifiers

PMID41664835
PMCPMC13016916

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.