Evidence map›Paper›PMID 41000231›Full record

ArticleAnnals of surgical treatment and research2025

Radiofrequency ablation for symptomatic, non-functioning thyroid nodules: a single-center retrospective observational study.

Huiyin Zhu, Lifen Bai, Ming Zhou, Ling Zhou

Abstract read
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In one paragraph

Article in Annals of surgical treatment and research, 2025. 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

4 authors.

Huiyin ZhuDepartment of Thyroid and Breast Surgery, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0003-1663-6161
Lifen BaiDepartment of Thyroid and Breast Surgery, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0009-0009-1815-0019
Ming ZhouDepartment of Thyroid and Breast Surgery, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0009-0002-4277-2394
Ling ZhouDepartment of Thyroid and Breast Surgery, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0003-2290-3756

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study was designed to investigate the efficacy and safety of radiofrequency ablation (RFA) for treating thyroid nodules (TNs). Methods: We retrospectively included the patients who underwent RFA for TNs in our hospital between January 2020 and December 2023. Preoperative and postoperative data including age, sex, body mass index, and thyroid hormone levels were collected from each patient. The efficacy of RFA on TNs was analyzed by evaluating the volume reduction (VR) and volume reduction rate (VRR) at postoperative months 1, 3, 6, and 12, respectively. In addition, the efficacy of RFA on nodule composition and postoperative complications was evaluated. Finally, a subgroup analysis was conducted to evaluate the potential risks. Results: VR at postoperative month 12 was significantly higher than those at postoperative months 1, 3, and 6, respectively (all P < 0.001). VRR at month 12 was significantly higher than that of months 1, 3, and 6, respectively (all P < 0.001). Conclusion: RFA is an effective option for treating TNs with good safety. VR and VRR showed significant increase after RFA compared with the pretreatment levels.

Indexed as

Benign noduleRadiofrequency ablationRetrospective studyThyroid noduleUltrasonography

Identifiers

PMID41000231
PMCPMC12457796

What OpenQuestion holds

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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.