Evidence map›Paper›PMID 24379701›Full record

ReviewInternational journal of women's health2013

Transvaginal ultrasonography in ovarian cancer screening: current perspectives.

John R van Nagell, John T Hoff

Open access · goldAbstract readReview
In one paragraph

Review in International journal of women's health, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 2 pooled it
6.0field-weighted citation impact, top 5% 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

38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
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  5. Review
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  7. Review
  8. Assessment of knowledge and awareness about ovarian cancer and its risk factors among women in Jazan, Saudi Arabia: a cross-sectional study.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025
    Article
  9. Article
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  11. Review
  12. Article
  13. Review
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  15. Article
  16. Article
  17. Article
  18. Article
  19. The Proteolytic Landscape of Ovarian Cancer: Applications in Nanomedicine.International journal of molecular sciences · 2022
    Review
  20. 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

2 authors at 1 institution in 1 country.

John R van NagellDepartment of Obstetrics and Gynecology, University of Kentucky Chandler Medical Center/Markey Cancer Center, Lexington, KY, USA.
John T HoffDepartment of Obstetrics and Gynecology, University of Kentucky Chandler Medical Center/Markey Cancer Center, Lexington, KY, USA.
Albert B. Chandler Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transvaginal ultrasonography (TVS) is an integral part of all major ovarian cancer screening trials. TVS is accurate in detecting abnormalities in ovarian volume and morphology, but is less reliable in differentiating benign from malignant ovarian tumors. When used as the only screening test, TVS is sensitive, but has a low positive predictive value. Therefore, serum biomarkers and tumor morphology indexing are used together with TVS to identify ovarian tumors at high risk for malignancy. This allows preoperative triage of high-risk cases to major cancer centers for therapy while decreasing unnecessary surgery for benign disease. Ovarian cancer screening has been associated with a decrease in stage at detection in most trials, thereby allowing treatment to be initiated when the disease is most curable.

Indexed as

ovarian cancerscreeningserum Ca-125ultrasound

Identifiers

PMID24379701
PMCPMC3873201
OpenAlexW1991909098

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.