Evidence map›Paper›PMID 42521232›Full record

ArticleAsia Pacific journal of clinical nutrition2026

Construction of a nomogram prediction model for opportunistic sarcopenia in patients with malignant tumors.

Liting Li, Ying Wang, Chunhua Bian, Hongmei Xue, Cong Wang, Jialu Hao, Zengning Li

Abstract read
In one paragraph

Article in Asia Pacific journal of clinical nutrition, 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

7 authors.

Liting Li *Department of Radiology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Ying Wang *Department of Nutrition, Affiliated Hospital of Hebei University of Engineering, Handan, China.
Chunhua BianDepartment of Nutrition, Tianjin Kanghui Hospital, TianJin, China.
Hongmei XueDepartment of Clinical Nutrition, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Cong WangDepartment of Oncology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Jialu HaoDepartment of Oncology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Zengning LiDepartment of Clinical Nutrition, The First Hospital of Hebei Medical University, Shijiazhuang, China. Email: zengningli@hebmu.edu.cn.

Funding

Medical Science Research Project of Hebei Provincial Health Commission 20231059
6 · The paper itself

Abstract

BACKGROUND AND

objectivesThe aim of this study was to establish a predictive model for opportunistic sarcopenia applicable to Chinese cancer patients, so as to quickly detect the occurrence of this condition and provide a basis for early clinical intervention. METHODS AND STUDY

designA total of 522 malignant tumor patients admitted to the First Hospital of Hebei Medical University from October 2017 to March 2022 were retrospectively analyzed. Opportunistic sarcopenia was diagnosed by L3 SMI. Twelve variables were collected; risk factors were screened and modeled via univariate and multivariate regression in R Studio, and con-tinuous variable cutoff points were determined by SPSS. A nomogram was constructed and validated with clinical decision and calibration curves.

resultsThe prevalence of opportunistic sarcopenia in the study population was 66.1% (345/522). The final predictive model included six key variables: gender, body mass index (BMI), C-reactive protein (CRP) level, systemic immune-inflammation index (SII), prognostic nutritional index (PNI), and SII-PNI. The area under the curve (AUC) of the model was 0.890 (95% CI: 0.854-0.926), indicating high discriminative ability. The calibration curve showed good consistency between the model's predictions and the actual diagnostic results. Clinical net benefit analysis showed that when the threshold range was greater than 0.6, the clinical benefit rate of the predictive model was higher than those of relative appendicular skeletal muscle mass (RASM) and appendicular skeletal muscle mass index (ASMI).

conclusionsThe constructed nomogram model can accurately estimate the probability of opportunistic sarcopenia in cancer patients, facilitating its early screening and targeted prevention.

Indexed as

NeoplasmsNomogramsSarcopeniaAdultAgedChinaFemaleHumansMaleMiddle AgedRetrospective StudiesRisk Factorsinflammatory indexmalignant tumorsnomogram prediction modelopportunistic sarcopeniascreening

Identifiers

PMID42521232
PMCPMC13413148

What OpenQuestion 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 OpenQuestion graph.