Evidence map›Paper›PMID 41809816›Full record

ArticleFrontiers in oncology2026

Bioelectrical impedance analysis-derived skeletal muscle mass index versus computed tomography for the detection of muscle mass reduction in patients with gastrointestinal cancer: a cross-sectional study.

Bo Gao, Qinggang Yuan, Hao Zhang, Wenqing Chen, Xiangrui Li, Xiaotian Chen

Abstract read
In one paragraph

Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Bo GaoDepartment of Clinical Nutrition, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Qinggang YuanDepartment of Gastrointestinal Surgery, Xuzhou Central Hospital, Xuzhou, China.
Hao ZhangDepartment of Clinical Nutrition, Jinshan Hospital of Fudan University, Shanghai, China.
Wenqing ChenDepartment of Clinical Nutrition, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Xiangrui LiDepartment of Clinical Nutrition, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Xiaotian ChenDepartment of Clinical Nutrition, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The aims of this study were to assess the precision of bioelectrical impedance analysis (BIA) in evaluating muscle mass and to establish a population-specific cutoff value for identifying muscle mass reduction in a Chinese population with gastric cancer. Methods: A total of 163 patients with gastric cancer were enrolled. Skeletal muscle mass was measured at the L3 level using computed tomography (CT) scans. Muscle mass was concurrently evaluated using BIA. The correlations of muscle mass between CT and BIA methods were assessed. Data consistency was analyzed by the intraclass correlation coefficient (ICC). The optimal cutoff value of the BIA-derived skeletal muscle index (SMI) for identifying muscle mass reduction was determined by receiver operating characteristic (ROC) curve analysis. Results: The mean skeletal muscle mass measured by CT and BIA was 118.81 ± 24.54 cm Conclusions: Muscle mass assessed by BIA showed a high correlation and satisfactory consistency with that measured by CT scan.

Indexed as

bioelectrical impedance analysisCT scangastric cancermuscle massnutrition

Identifiers

PMID41809816
PMCPMC12967928

What Socratic 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 Socratic graph.