Evidence map›Paper›PMID 32436637›Full record

ReviewMolecular genetics & genomic medicine2020

Indeterminate thyroid nodules in the era of molecular genomics.

Sarika N Rao, Victor Bernet

Open access · goldAbstract readReview
In one paragraph

Review in Molecular genetics & genomic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.0field-weighted citation impact, top 12% of its field
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

11 citing papers in PubMed, 23 citations in OpenAlex.

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  11. Indeterminate thyroid nodules in the era of molecular genomics.Molecular genetics & genomic medicine · 2020
    Review
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

2 authors at 1 institution in 1 country.

Sarika N RaoDivision of Endocrinology, Mayo Clinic, Jacksonville, FL, USA.ORCID 0000-0003-2141-1696
Victor BernetDivision of Endocrinology, Mayo Clinic, Jacksonville, FL, USA.ORCID 0000-0002-2477-5631
Jacksonville College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIndeterminate thyroid nodules are diagnosed in up to 30% of fine-needle aspirations and the risk of malignancy in these cases are highly variable. Consequently, managing these nodules has been a challenge. While a diagnostic thyroidectomy would help clarify the pathology, there is the risk of developing surgical-related complications for a procedure that may not have been necessary and associated high costs. Genomic testing of indeterminate thyroid nodules may help better guide management.

methodsWe present an unbiased comprehensive review of available molecular testing for classifying indeterminate thyroid nodules, as well as their strengths and limitations, with the objective to allow practitioners to choose the best testing modality for their patients.

resultsMolecular testing of these nodules provided a platform to help distinguish benign versus malignant nodules, providing more confidence to rule in or rule out the likelihood of thyroid cancer in indeterminate nodules.

conclusionGenomic testing has evolved to more comprehensive panels to better stratify indeterminate nodules, including Hürthle cell neoplasms and noninvasive follicular neoplasm with papillary-like nuclear features. Understanding the methodology of each available test improves patient care and reduces unnecessary costs.

Indexed as

Biopsy, Fine-NeedleGenetic TestingHumansReagent Kits, DiagnosticSequence Analysis, DNAThyroid NoduleReagent Kits, Diagnosticindeterminate thyroid nodulesthyroid cancerThyroseq

Identifiers

PMID32436637
PMCPMC7503096
OpenAlexW3027511849

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.