Evidence map›Paper›PMID 38993430›Full record

ArticleCureus2024

Diagnostic Accuracy of Fine-Needle Aspiration Cytology in Cervical Lymphadenopathies at a Tertiary Care Center in the Kingdom of Bahrain.

Kawthar Qader, Maryam Qader, Aalaa Mubarak, Khadija Alaradi

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Kawthar QaderOtolaryngology - Head and Neck Surgery, Salmaniya Medical Complex, Manama, BHR.
Maryam QaderOtolaryngology - Head and Neck Surgery, Arabian Gulf University, Manama, BHR.
Aalaa MubarakLaboratory and Pathology, Salmaniya Medical Complex, Manama, BHR.
Khadija AlaradiOtolaryngology - Head and Neck Surgery, Salmaniya Medical Complex, Manama, BHR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction  Fine-needle aspiration cytology (FNAC) has become widely used as a first-line diagnostic tool in the evaluation of cervical lymphadenopathies (LADs). However, there are conflicting reports regarding its accuracy in differentiating between malignant and benign pathologies. In this study, we aim to determine the reliability of FNAC in distinguishing between benign and malignant pathologies causing cervical LAD. Methods This is a cross-sectional study reviewing the electronic medical records of all patients who underwent both FNAC and excisional biopsy of cervical LADs between January 2016 and December 2023 at a tertiary care center in the Kingdom of Bahrain. A comparison was conducted between the cytopathological results obtained by FNAC and the histopathological results obtained by excisional biopsy to determine the diagnostic accuracy of FNAC. Results In the study period, 83 patient records were reviewed and included in the data analysis. Fine-needle aspiration cytology yielded a sensitivity of 89.3%, a specificity of 55.6%, a positive predictive value (PPV) of 72.4%, a negative predictive value (NPV) of 80.0%, and an overall accuracy of 74.7% in diagnosing cervical LADs. Conclusion Despite FNAC being accessible, convenient, and cost-effective, it has certain limitations that can restrict its accuracy in diagnosing lymphomas. We recommend further studies to research these limitations and the possible tools, such as ancillary testing, that may be useful in overcoming them.

Indexed as

cervical lymphadenopathydiagnostic test accuracyexcisional biopsyfine-needle aspiration cytologyfine-needle aspiration cytology (fnac)histological diagnosis

Identifiers

PMID38993430
PMCPMC11238613

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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.