Evidence map›Paper›PMID 42125632›Full record

Article... International Workshop on Spoken Dialogue Systems Technology. International Workshop on Spoken Dialogue Systems Technology2026

ReflectOR: an LLM-based Agent for Post-Operative Surgical Debriefing.

Lorenzo Fumi, Marco Bombieri, Sara Allievi, Stefano Bonvini, Theodora Chaspari, Marco Zenati, Paolo Giorgini

Abstract read
In one paragraph

Article in ... International Workshop on Spoken Dialogue Systems Technology. International Workshop on Spoken Dialogue Systems Technology, 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
–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

7 authors.

Lorenzo FumiDepartment of Information Engineering and Computer Science, University of Trento, Italy.
Marco BombieriDepartment of Information Engineering and Computer Science, University of Trento, Italy.
Sara AllieviAzienda Provinciale per i Servizi Sanitari, Trento, Italy.
Stefano BonviniAzienda Provinciale per i Servizi Sanitari, Trento, Italy.
Theodora ChaspariInstitute of Cognitive Science and Department of Computer Science, University of Colorado, Boulder, USA.
Marco ZenatiMedical Robotics and Computer Assisted Surgery (MRCAS) Laboratory, Division of Cardiac Surgery, Veterans Affairs Boston Healthcare System, Boston, MA, USA.
Paolo GiorginiDepartment of Information Engineering and Computer Science, University of Trento, Italy.

Funding

Developing Standardized Intraoperative Process Models to Enhance Surgical SafetyR01HL126896 · NHLBI · HARVARD MEDICAL SCHOOL · PI ZENATI, MARCO A. · 2016 to 2023
$3.7M
NHLBI NIH HHS R01 HL126896
6 · The paper itself

Abstract

Ineffective teamwork and communication can generate medical errors in the high-pressure environment of surgery, making post-operative debriefings essential for enhancing team performance and patient safety. However, these sessions are frequently rushed or incomplete due to clinicians' limited time. This paper introduces ReflectOR, an Agentic-AI architecture designed to support surgical debriefings by processing audio recordings from the operating room. The system employs specialized sub-agents that perform tasks such as generating summaries, constructing timelines of intraoperative events, identifying potential errors, and counting the materials used. A qualitative evaluation indicates that the system effectively contextualizes transcripts, demonstrating its potential as a valuable tool for surgical debriefing. The paper also outlines key considerations for applying such an architecture in real-world clinical environments.

Identifiers

PMID42125632
PMCPMC13159939

What OpenQuestion holds

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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.