Evidence map›Paper›PMID 39529928›Full record

ArticleFrontiers in nutrition2024

Nutritional assessment of Chinese gynecologic cancer survivors with post-surgical lower limb lymphedema: a cross-sectional study.

Xiaoling Zhu, Xinjun Liu, Jinbo Tian, Chunshui He, Shan Huang

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Xiaoling ZhuDepartment of Vascular Surgery, Chengdu DongLi Hospital, Chengdu, Sichuan, China.
Xinjun LiuDepartment of Vascular Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Jinbo TianDepartment of Vascular Surgery, Chengdu DongLi Hospital, Chengdu, Sichuan, China.
Chunshui He *Department of Vascular Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Shan Huang *Department of Oncology, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to evaluate the nutritional status of Chinese gynecologic cancer survivors with post-surgical lower limb lymphedema (LLL) by analyzing serum albumin levels and the prognostic nutritional index (PNI). Methods: A retrospective cross-sectional study was conducted at two Chinese medical centers. The cohort comprised 155 gynecologic cancer survivors who developed symptomatic unilateral LLL post-surgery, during the period from September 2021 to June 2024. Nutritional status was assessed by measuring serum albumin and PNI. Statistical analysis was performed using univariate and multivariate logistic regression models to identify factors associated with low serum albumin and PNI. Results: The mean age of the cohort was 55.47 ± 10.56 years, and the median total survival time was 72.0 months (36.5, 111.5 months). The prevalence of low serum albumin (< 40 g/L) was 40.0%, and low PNI (< 45) was 80.6%. Significant associations were found between low hemoglobin levels and both low serum albumin (OR = 1.05, 95% CI: 1.02-1.08, Conclusion: The study highlights a significant prevalence of high risk of malnutrition among Chinese gynecologic cancer survivors with LLL, underscoring the need for regular nutritional assessments and interventions.

Indexed as

gynecologic cancerslower limb lymphedemanutritional statusPrognostic Nutritional Indexserum albumin

Identifiers

PMID39529928
PMCPMC11551780

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.