Evidence map›Paper›PMID 35446712›Full record

ArticleAnnals of the Royal College of Surgeons of England2023

Comparison of fine-needle aspiration with fine-needle capillary cytology in thyroid nodules.

H Hatami, M Samsami, S Movahedinia, B Salehi, M Movahedinia, M Ardeshir

Open access · greenAbstract read
In one paragraph

Article in Annals of the Royal College of Surgeons of England, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 26% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

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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

6 authors at 3 institutions in 1 country.

H HatamiShahid Beheshti University of Medical Sciences, Tehran, Iran.
M SamsamiShahid Beheshti University of Medical Sciences, Tehran, Iran.
S MovahediniaShahid Beheshti University of Medical Sciences, Tehran, Iran.
B SalehiKerman University of Medical Sciences, Kerman, Iran.
M MovahediniaShahid Beheshti University of Medical Sciences, Tehran, Iran.
M ArdeshirTehran University of Medical Sciences, Tehran, Iran.
Shahid Beheshti University of Medical Sciences · IRKerman University of Medical Sciences · IRTehran University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionHigh false-negative results have been reported for fine-needle aspiration (FNA) cytology in thyroid nodules. Fine-needle capillary (FNC) cytology is an alternative technique that prevents aspiration, reducing tissue damage. This study aimed to compare FNA and FNC in assessing thyroid nodules and in terms of their predictive role in the appropriate diagnosis of malignancy.

methodsThis is a comparative prospective study conducted on 486 patients. FNA was performed in 235 patients during 2016 and 2017 and FNC in 251 patients during 2018 and 2019. The quality of cytological specimens was compared and then correlated with the final histopathological findings of 39 patients who underwent thyroidectomy.

resultsBoth groups were statistically similar regarding age and sex distribution. The FNA technique yielded significantly higher adequate specimens compared with FNC (

conclusionsThe two methods, FNA and FNC, did not differ in terms of overall quality. FNA was superior regarding consistency with the histopathological results and the ability to diagnose malignancy.

Indexed as

Thyroid NoduleBiopsy, Fine-NeedleHumansProspective StudiesSensitivity and SpecificityThyroidectomyBethesdaCytologyFine-needle aspirationFine-needle capillaryThyroid nodule

Identifiers

PMID35446712
PMCPMC9889172
OpenAlexW4224243678

What OpenQuestion holds

Textmetadata
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.