Evidence map›Paper›PMID 41814046›Full record

SynthesisLangenbeck's archives of surgery2026

Simulation unlocked: transforming thyroid surgery training through high-fidelity and curriculum-based innovation-a systematic review.

Daqi Zhang, Francesco Brucchi, Diego Barbieri, Hoon Yub Kim, Carla Colombo, Gianluca Donatini, Paolo Miccoli, Wu Che-Wei, Gianlorenzo Dionigi, Lanlan Wan

Abstract readSystematic Review
In one paragraph

Synthesis in Langenbeck's archives 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.

Daqi ZhangDivision of Thyroid Surgery, Jilin Provincial Key Laboratory of Thyroid Disease, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, China-Japan Union Hospital of Jilin University, Changchun City, Jilin Province, China.
Francesco BrucchiDivision of General Surgery, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Auxologico Italiano, Milan, Italy.
Diego BarbieriDivision of Head and Neck, Otorhinolaryngology Unit, San Raffaele Scientific Institute IRCCS (Istituto Di Ricovero E Cura a Carattere Scientifico), Milan, Italy.
Hoon Yub KimDepartment of Surgery, KUMC Thyroid Center, Korea University College of Medicine, Korea University Hospital, Seoul, Korea.
Carla ColomboDivision of Endocrinology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Auxologico Italiano, Milan, Italy.
Gianluca DonatiniDepartment of General and Endocrine Surgery, University of Poitiers, CHU Poitiers, Poitiers, France.
Paolo MiccoliDepartment of Surgical, Medical, Pathology, and Critical Care, The University of Pisa, 56124, Pisa, Italy.
Wu Che-WeiDepartment of Otolaryngology-Head and Neck Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Gianlorenzo DionigiDivision of General Surgery, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Auxologico Italiano, Milan, Italy.
Lanlan WanDepartment of Anesthesia, The Second Hospital of Jilin University, No.218 Ziqiang Street, Nanguan District, Changchun City, Jilin Province, 130000, China. wanll@jlu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSimulation-based and structured training have transformed surgical education, providing safe environments for skill acquisition and performance assessment. However, evidence in thyroid surgery remains fragmented across modalities such as simulation, video-based learning, intraoperative neuromonitoring (IONM), and fellowship programs. This systematic review aimed to synthesize primary evidence on educational interventions in thyroid surgery and their impact on technical performance and patient outcomes.

methodsA systematic search of PubMed, Embase, Scopus, and Web of Science was conducted up to April 2025. Eligible studies included randomized trials, prospective or retrospective cohorts, and validation studies assessing simulation-based, structured, or supervised training. Two reviewers independently screened and extracted data, assessing quality using RoB 2, ROBINS-I, or CASP. Due to heterogeneity, results were summarized through a structured narrative synthesis by training modality.

resultsTen primary studies (2011–2025) were included. Simulation and virtual-reality training improved technical performance, task completion time, and perceived realism. Supervised resident training reduced operative time after 30–50 cases, approximating senior benchmarks. Video-assisted learning decreased error rates by 49% and staff takeovers by 52%. IONM-based education lowered recurrent laryngeal nerve injury rates and enhanced procedural standardization. Fellowship-trained surgeons showed shorter operative times and fewer complications compared with non-fellowship peers. Across all modalities, training interventions improved either performance or safety outcomes.

conclusionsStructured educational programs—including simulation, supervised learning, and IONM or fellowship curricula—enhance surgical performance and safety in thyroid surgery. Despite limited quantitative evidence, findings support integrating multimodal, competency-based education into surgical training. Standardized outcome measures and multicenter validation are needed to define optimal learning pathways.

Indexed as

Clinical CompetenceCurriculumHigh Fidelity Simulation TrainingSimulation TrainingThyroidectomyHumansFellowship trainingIntraoperative neuromonitoringLearning curveNarrative synthesisSimulationSurgical educationThyroid surgeryvirtual reality

Identifiers

PMID41814046
PMCPMC13056755

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.