Evidence map›Paper›PMID 40634322›Full record

SynthesisScientific reports2025

Predicting the prognosis of breast cancer patients by using pan-immune-inflammation value: a systematic review and meta-analysis.

Patricia Diana Prasetiyo, Alif Rizky Soeratman, Bernard Agung Baskoro, Stella Marleen

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Patricia Diana PrasetiyoDepartment of Pathology, Faculty of Medicine, Pelita Harapan University, Tangerang, 15811, Banten, Indonesia. drpdiana@yahoo.com.ORCID http://orcid.org/0000-0002-7514-6923
Alif Rizky SoeratmanDivision of Surgical Oncology, Department of Surgery, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, DKI Jakarta, Indonesia.
Bernard Agung BaskoroDivision of Surgical Oncology, Department of Surgery, Faculty of Medicine, Pelita Harapan University, Karawaci, Tangerang, 15811, Indonesia.
Stella MarleenDepartment of Pathology, Faculty of Medicine, Pelita Harapan University, Tangerang, 15811, Banten, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Available evidence has unequivocally established the significance of inflammation in the onset and advancement of cancer. Pan-immune-inflammation value (PIV) is an evaluation method that incorporates the role of different white blood cells (WBC) subsets to predict the progression of cancer. The main aim of this study is to assess the feasibility of the PIV score in predicting prognosis among women with breast cancer. A thorough search was performed on the Scopus, Europe PMC, Cochrane Library, and Medline databases up until September 25th, 2024, employing a combination of pertinent keywords. This investigation includes paper that explores the connection of PIV and survival in breast neoplasm. We utilized random-effects models to assess the hazard ratio (HR) and report the results together with their respective 95% confidence intervals (CI). Sixteen papers were chosen for analysis. The findings of our meta-analysis from multivariate data indicated that breast cancer patients who possessed high PIV score had a higher risk of shorter overall survival (OS) (HR 2.22; 95% CI 1.60-3.07, p < 0.00001, I

Indexed as

Breast NeoplasmsInflammationFemaleHumansPrognosisBreast cancerInflammationOncologyPan-immune-inflammation valuePrognosis

Identifiers

PMID40634322
PMCPMC12241612

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.