Evidence map›Paper›PMID 42283629›Full record

ArticleCritical care explorations2026

Multiagent First-Person Perspective Analysis for Leadership Assessment in Pediatric Emergency Simulations: A Feasibility Study.

Hisataka Nozawa, Ayumi Kunikata, Yuki Sakai, Liangyang Ouyang, Ryosuke Furuta, Yoichi Sato, Hikoro Matsui

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Article in Critical care explorations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hisataka NozawaDepartment of Pediatrics, The University of Tokyo Hospital, Tokyo, Japan.ORCID 0000-0002-0969-3304
Ayumi KunikataDepartment of Pediatrics, The University of Tokyo Hospital, Tokyo, Japan.
Yuki SakaiInstitute of Industrial Science, The University of Tokyo, Tokyo, Japan.
Liangyang OuyangInstitute of Industrial Science, The University of Tokyo, Tokyo, Japan.
Ryosuke FurutaInstitute of Industrial Science, The University of Tokyo, Tokyo, Japan.
Yoichi SatoInstitute of Industrial Science, The University of Tokyo, Tokyo, Japan.
Hikoro MatsuiDepartment of Pediatrics, The University of Tokyo Hospital, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConventional leadership assessments provide structured ratings but offer limited observer-independent metrics. Because leadership and teamwork are interdependent, capturing team dynamics requires multiagent, time- and space-synchronized analysis. This pilot study examined the feasibility of applying multiagent, spatiotemporally synchronized first-person perspective analysis (FPA) in emergency simulations.

methodsThis exploratory simulation study was conducted in a real patient-care room within the PICU of our institution. Two physicians-a critical care specialist (L1) and a pediatric resident (L2)-each completed one scenario as team leader. Leadership behaviors were evaluated using the Ottawa Global Rating Scale, focusing on Situational Awareness and Communication. Wearable smart glasses recorded first-person video and gaze data, enabling identification of fixations within five predefined areas ("patient," "member," "device," "screen," and "unknown"). Eye-contact events were quantified using face detection based on You Only Look Once, version 8, a deep learning-based object detection model, combined with gaze-vector estimation, and analyzed in relation to the timing of verbal communication.

resultsMultiagent, time-synchronized FPA was implemented without device- or software-related interruption, and no critical data loss occurred. The mean analyzable session duration relative to total operating time was 77.1%, and the mean percentage of frames excluded from object tracking was 29.3%, primarily due to target objects being outside the field of view. As secondary, illustrative findings, Ottawa Global Rating Scale scores indicated that both leaders were rated within the competent range. FPA further showed that both leaders spent approximately 80% of the scenario fixating on predefined relevant areas (L1: 599/731 s; L2: 565/693 s). Greater patient-directed fixation and balanced gaze transitions were observed in L1, whereas L2 showed prolonged fixation within nonspecific areas. L1 also established eye contact with team members more frequently during direct orders or information sharing.

conclusionsThis study demonstrates the feasibility of multiagent, time-synchronized FPA for extracting gaze-based behavioral information relevant to leadership assessment in emergency simulations.

Indexed as

LeadershipPatient Care TeamPediatricsSimulation TrainingFeasibility StudiesHumansIntensive Care Units, PediatricPilot ProjectsSmart Glassesadvanced cardiac life supportartificial intelligencecrew resource managementhealthcareleadershipmedical education

Identifiers

PMID42283629
PMCPMC13263087

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LicenceCC BY-NC-ND
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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.