Evidence map›Paper›PMID 41398789›Full record

ReviewMedicine2025

Perspective in radiofrequency ablation for benign thyroid nodules: A perspective.

Zhuan-Ning Han, Xu-Ping Shen, Rong-Rong Wang

Abstract readReview
In one paragraph

Review in Medicine, 2025. 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. Observational
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

3 authors.

Zhuan-Ning HanDepartment of Ultrasound, The Second Affiliated Hospital of Xi'an Medical College, Xi'an, China.
Xu-Ping ShenDepartment of Ultrasound, The First People's Hospital of Tongxiang, Tongxiang, China.
Rong-Rong WangDepartment of Function, Xi'an Hospital of Traditional Chinese Medicine, Xi'an, China.ORCID 0009-0004-3195-4904

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiofrequency ablation (RFA) represents a paradigm shift in the minimally invasive management of benign thyroid nodules (BTNs). By utilizing high-frequency radio waves to generate thermal energy, RFA precisely targets and ablates pathological thyroid tissue, offering a substantial improvement over conventional surgical methods. The procedure minimizes trauma, expedites patient recovery, and significantly reduces postoperative complications. Robust empirical evidence supports RFA's efficacy in reducing BTNs volume while preserving the structural and functional integrity of the surrounding thyroid parenchyma. Technological advancements, such as real-time ultrasound guidance, have enhanced both the precision and safety of RFA procedures. Despite these advancements, gaps remain in the understanding of RFA's optimal implementation. Further research is needed to refine best practices, evaluate long-term therapeutic outcomes, and establish clearer patient selection criteria. The ongoing advancements in RFA not only improve procedural accuracy but also promise better clinical outcomes, thereby increasing its accessibility and comprehension among healthcare providers and patients. This potential for broader clinical adoption positions RFA as a viable mainstream treatment for BTNs. Anticipated future directions encompass the development of innovative RFA devices, the refinement of therapeutic protocols, and the integration of RFA into multidisciplinary treatment models aimed at enhancing efficacy and safety for patients with BTNs.

Indexed as

Catheter AblationRadiofrequency AblationThyroid NoduleHumansTreatment OutcomeUltrasonography, InterventionalBenign thyroid nodulescomplicationperspectiveradiofrequency ablationsafetytreatment

Identifiers

PMID41398789
PMCPMC12708199

What OpenQuestion holds

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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.