Evidence map›Paper›PMID 41739839›Full record

ArticlePloS one2026

Physiology-driven reversible dynamic synthetic tissue model for responsive moulage in infected wound training.

Alex T Gong, Aleah M DeSchmidt, Austin J Baird, Jessica G Gonzaga, Shi-Wen Olivia Yau, Rudolph J Toepfer, Jessica Zhang, Conner J Parsey, Rainer Leuschke, Jack E Norfleet and 1 more

Abstract read
In one paragraph

Article in PloS one, 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

11 authors.

Alex T GongDepartment of Surgery, Division of Healthcare Simulation Science, University of Washington, Seattle, Washington, United States of America.ORCID https://orcid.org/0000-0001-6519-8458
Aleah M DeSchmidtDepartment of Surgery, Division of Healthcare Simulation Science, University of Washington, Seattle, Washington, United States of America.
Austin J BairdDepartment of Surgery, Division of Healthcare Simulation Science, University of Washington, Seattle, Washington, United States of America.
Jessica G GonzagaDepartment of Surgery, Division of Healthcare Simulation Science, University of Washington, Seattle, Washington, United States of America.
Shi-Wen Olivia YauDepartment of Surgery, Division of Healthcare Simulation Science, University of Washington, Seattle, Washington, United States of America.
Rudolph J ToepferDepartment of Surgery, Division of Healthcare Simulation Science, University of Washington, Seattle, Washington, United States of America.
Jessica ZhangDepartment of Surgery, Division of Healthcare Simulation Science, University of Washington, Seattle, Washington, United States of America.
Conner J ParseyMedical Simulation Research Branch Simulation and Training Technology Center, United States of America Army DEVCOM Soldier Center, Orlando, Florida, United States of America.
Rainer LeuschkeDepartment of Surgery, Division of Healthcare Simulation Science, University of Washington, Seattle, Washington, United States of America.
Jack E NorfleetMedical Simulation Research Branch Simulation and Training Technology Center, United States of America Army DEVCOM Soldier Center, Orlando, Florida, United States of America.ORCID https://orcid.org/0000-0001-8643-0973
Robert M SweetDepartment of Surgery, Division of Healthcare Simulation Science, University of Washington, Seattle, Washington, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Misidentification of dermatologic manifestations of systemic infection can lead to life-threatening developments including sepsis. Training medical providers to accurately identify and respond to infections using simulation could reduce these rates. Moulage of infections are static and require a manual "scene change" during simulation scenarios. A high-fidelity, automated physiology-driven dynamic infection model was developed using the Modular Healthcare Simulation and Education platform that physically changes to simulate the progression of an infected wound. The silicone model was designed using resistive wire, wax actuators, and thermochromic powder. The resistive wire heats the wax, resulting in focal edema, skin warmth, and erythema that reverses with proper recognition and treatment. Without external treatment, purulence is released, and the patient will develop septic physiology. To evaluate the physiological realism and training value of the model, a survey of physicians was conducted using a five-point Likert scale of agreement with 5 being the highest rating. When asked to rate the realism of the model, the mean response was 4.1 ± 0.7. When asked if they thought the model had value for training infection identification, the mean response was 4.3 ± 0.9. Finally, when asked if the dynamic aspect would improve the simulation, the mean response was 4.7 ± 0.6. The dynamic infection model is functional and appealing for practitioners to assist in the early detection of infection. The use of a dynamic training model could potentially be used to replicate other dermatologic manifestations of systemic disease processes and improve medical training.

Indexed as

Models, BiologicalWound InfectionHumans

Identifiers

PMID41739839
PMCPMC12935211

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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.