Evidence map›Paper›PMID 41509921›Full record

ArticleiScience2026

An explainable machine learning model predicts 30-day readmission after vertebral augmentation.

Chen Liu, Qingyang Fu, Wang Qifei, Weixiao Sun, Shimin Tang, Zhe Wang, Lianying Ma, Yunze Feng, Xu Zhai, Chunlin Li and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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

14 authors.

Chen LiuDepartment of Orthopedics, Qilu Hospital, Shandong University, Jinan, Shandong 250000, China.
Qingyang FuDepartment of Orthopedics, Qilu Hospital, Shandong University, Jinan, Shandong 250000, China.
Wang QifeiThe School of Physics and Electronics, Shandong Normal University, Jinan 250358, China.
Weixiao SunOperating Room, Qilu Hospital of Shandong University, Jinan, China.
Shimin TangThe School of Physics and Electronics, Shandong Normal University, Jinan 250358, China.
Zhe WangThe School of Physics and Electronics, Shandong Normal University, Jinan 250358, China.
Lianying MaThe School of Physics and Electronics, Shandong Normal University, Jinan 250358, China.
Yunze FengDepartment of Orthopedics, Qilu Hospital, Shandong University, Jinan, Shandong 250000, China.
Xu ZhaiDepartment of Orthopedics, Qilu Hospital, Shandong University, Jinan, Shandong 250000, China.
Chunlin LiDepartment of Orthopedics, Qilu Hospital, Shandong University, Jinan, Shandong 250000, China.
Wanlong XuDepartment of Orthopedics, Qilu Hospital, Shandong University, Jinan, Shandong 250000, China.
Wencan ZhangDepartment of Orthopedics, Qilu Hospital, Shandong University, Jinan, Shandong 250000, China.
Le LiDepartment of Orthopedics, Qilu Hospital, Shandong University, Jinan, Shandong 250000, China.
Haipeng SiDepartment of Orthopedics, Qilu Hospital, Shandong University, Jinan, Shandong 250000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteoporotic vertebral compression fracture (OVCF) patients face high 30-day readmission risks after vertebral augmentation procedures (VAPs). Using electronic health records (EHRs) of 3,947 OVCF patients who underwent VAPs (2019-2024), we developed an interpretable machine learning model to identify readmission predictors. Eight algorithms were evaluated via 10-fold cross-validation, and XGBoost showed the best performance (area under the curve [AUC], sensitivity, specificity, F1 score, and decision curve analysis). SHapley Additive exPlanations (SHAPs) analysis revealed key predictors including frailty, fall history, prolonged hospitalization, comorbidities (pulmonary/kidney disease), advanced age, and hypoalbuminemia. A clinical web application was created for real-time risk stratification, visualizing individualized risk contributions via SHAP to enable proactive interventions and targeted prevention, thereby improving outcomes and reducing healthcare burden.

Indexed as

Machine learningOrthopedics

Identifiers

PMID41509921
PMCPMC12774678

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.