Evidence map›Paper›PMID 39286268›Full record

ArticleFrontiers in endocrinology2024

Corrigendum: Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance.

Si Eun Lee, Hye Jung Kim, Hae Kyoung Jung, Jin Hyang Jung, Jae-Han Jeon, Jin Hee Lee, Hanpyo Hong, Eun Jung Lee, Daham Kim, Jin Young Kwak

Abstract readPublished Erratum
In one paragraph

Article in Frontiers in endocrinology, 2024. 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

5 · Who and what money

Authors and funding

10 authors.

Si Eun LeeDepartment of Radiology, Yongin Severance Hospital, College of Medicine, Yonsei University, Yongin-si, Republic of Korea.
Hye Jung KimDepartment of Radiology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
Hae Kyoung JungDepartment of Radiology, CHA University Bundang Medical Center, Seongnam-si, Republic of Korea.
Jin Hyang JungDepartment of Surgery, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
Jae-Han JeonDepartment of Endocrinology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
Jin Hee LeeDepartment of Radiology, Keimyung University Dongsan Hospital, Daegu, Republic of Korea.
Hanpyo HongDepartment of Radiology, Yongin Severance Hospital, College of Medicine, Yonsei University, Yongin-si, Republic of Korea.
Eun Jung LeeDepartment of Computational Science and Engineering, Yonsei University, Seoul, Republic of Korea.
Daham KimDepartment of Endocrinology, College of Medicine, Yonsei University, Seoul, Republic of Korea.
Jin Young KwakDepartment of Radiology, College of Medicine, Yonsei University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

[This corrects the article DOI: 10.3389/fendo.2024.1372397.].

Indexed as

artificial intelligencedigital learninglearningthyroid cancerultrasound

Identifiers

PMID39286268
PMCPMC11403210

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.