Evidence mapPaperPMID 41081983Full record

ReviewJournal of applied genetics2025

Potential biomarkers for early detection of endometriosis: current state of art (what we know so far).

Michalina Kliber-Gałuszka, Klaudia Kulczyńska-Figurny, Paweł Piotr Jagodziński, Andrzej Pławski

Abstract readReview
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In one paragraph

Review in Journal of applied genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Building Disease Models for Endometriosis: iPSCs as Game-Changers.International journal of molecular sciences · 2026
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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

4 authors.

Michalina Kliber-GałuszkaDepartment of Biochemistry and Molecular Biology, Poznan University of Medical Sciences, Święcickiego 6 St., 61-701, Poznan, Poland.
Klaudia Kulczyńska-FigurnyDepartment of Biochemistry and Molecular Biology, Poznan University of Medical Sciences, Święcickiego 6 St., 61-701, Poznan, Poland. kkulczynska-figurny@ump.edu.pl.ORCID http://orcid.org/0000-0003-3259-8097
Paweł Piotr JagodzińskiDepartment of Biochemistry and Molecular Biology, Poznan University of Medical Sciences, Święcickiego 6 St., 61-701, Poznan, Poland.ORCID http://orcid.org/0000-0002-9046-6802
Andrzej PławskiInstitute of Human Genetics, Polish Academy of Sciences, Strzeszyńska 32 St., 60-479, Poznan, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endometriosis is a chronic gynecological condition characterized by the presence of endometrial-like tissue outside the uterine cavity. Its diagnosis remains a significant clinical challenge, often delayed by 7 to 12 years, leading to considerable socio-economic burden and a substantial decline in patients' quality of life, including potential infertility. Consequently, there is an urgent need to identify reliable biomarkers that would allow for earlier and more accurate detection. This review provides a comprehensive and up-to-date analysis of potential biomarkers for the diagnosis of endometriosis, including hormonal, inflammatory, genetic, epigenetic, immunological, metabolic, and imaging-based markers. Their diagnostic value and limitations are critically assessed, with particular emphasis on the advantages of multimarker and integrated diagnostic approaches to enhance early detection. The findings of this review offer valuable insights for clinicians, researchers, and healthcare professionals working to develop better diagnostic methods and improve patient outcomes. Moreover, the integration of emerging technologies, such as artificial intelligence, offers promising opportunities to revolutionize endometriosis diagnostics through personalized and precise medical care.

Indexed as

Artificial intelligenceBiomarkersEndometriosisMachine learningPersonalized medicine

Identifiers

What Socratic 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 Socratic graph.