Evidence map›Paper›PMID 20350313›Full record

ArticleJournal of ovarian research2010

The detection, treatment, and biology of epithelial ovarian cancer.

Jennifer Aa Gubbels, Nick Claussen, Arvinder K Kapur, Joseph P Connor, Manish S Patankar

Open access · goldAbstract read
In one paragraph

Article in Journal of ovarian research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
10.1field-weighted citation impact, top 2% 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

34 citing papers in PubMed, 89 citations in OpenAlex.

  1. Article
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  10. An enzymatic toolkit for selective proteolysis, detection, and visualization of mucin-domain glycoproteins.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  11. Article
  12. The Many Microenvironments of Ovarian Cancer.Advances in experimental medicine and biology · 2020
    Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Differential expression of ligands for NKG2D and DNAM-1 receptors by epithelial ovarian cancer-derived exosomes and its influence on NK cell cytotoxicity.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016
    Article
  19. Article
  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

5 authors at 2 institutions in 1 country.

Jennifer Aa GubbelsDepartment of Obstetrics and Gynecology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA. jpconnor@wisc.edu.
Nick Claussen
Arvinder K Kapur
Joseph P Connor
Manish S Patankar
University of Wisconsin–Madison · USAugustana University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer is particularly insidious in nature. Its ability to go undetected until late stages coupled with its non-descript signs and symptoms make it the seventh leading cause of cancer related deaths in women. Additionally, the lack of sensitive diagnostic tools and resistance to widely accepted chemotherapy regimens make ovarian cancer devastating to patients and families and frustrating to medical practitioners and researchers. Here, we provide an in-depth review of the theories describing the origin of ovarian cancer, molecular factors that influence its growth and development, and standard methods for detection and treatment. Special emphasis is focused on interactions between ovarian tumors and the innate and adaptive immune system and attempts that are currently underway to devise novel immunotherapeutic approaches for the treatment of ovarian tumors.

Identifiers

PMID20350313
PMCPMC2856581
OpenAlexW2166162078

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.