Evidence map›Paper›PMID 41402560›Full record

ArticleNPJ digital medicine2025

Prognostic model and U-shaped SBP-risk correlation in unstable pelvic fractures with TBI.

Shuming Du, Xiaolong Zheng, Yaxu Zhang, Qiang Xiang, Changgui Zhang

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. 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

5 authors.

Shuming Du *Department of Emergency Medicine, The First Affiliated Hospital (Southwest Hospital) of Army Medical University, Chongqing, China.
Xiaolong Zheng *Department of Emergency Medicine, The First Affiliated Hospital (Southwest Hospital) of Army Medical University, Chongqing, China.
Yaxu ZhangDepartment of Emergency Medicine, The First Affiliated Hospital (Southwest Hospital) of Army Medical University, Chongqing, China.
Qiang XiangDepartment of Emergency Medicine, The First Affiliated Hospital (Southwest Hospital) of Army Medical University, Chongqing, China. xqiang@tmmu.edu.cn.
Changgui ZhangDepartment of Emergency Medicine, The First Affiliated Hospital (Southwest Hospital) of Army Medical University, Chongqing, China. ChangguiZhang@tmmu.edu.cn.

Funding

the Key Joint Project of Chongqing Science and Technology Bureau and Chongqing Health Commission (2024ZDXM034
6 · The paper itself

Abstract

Patients with unstable pelvic fractures combined with traumatic brain injury (TBI) present complex conditions, with pathophysiological contradictions in clinical treatment between limited fluid resuscitation and maintenance of cerebral perfusion pressure. A retrospective analysis was conducted on 204 patients with unstable pelvic fractures combined with TBI between January 1, 2010, and May 31, 2025. A Nomogram model was constructed via LASSO and multivariate logistic regression, with simultaneous development of an interpretable machine learning prediction model. Results identified shock, abnormal systolic blood pressure (SBP), non-surgical management, and prolonged coagulation time as independent poor prognosis risk factors. The Nomogram constructed using SBP, shock, pelvic fracture surgery, and prothrombin time (PT), showed good performance (AUC = 0.801, C-index = 0.7747). Poor prognosis risk was lowest at SBP 110-122 mmHg; a U-shaped correlation existed between SBP and prognosis. The Nomogram and interpretable XGBoost model provide a basis for individualized blood pressure management, coagulation monitoring, and surgical decision-making. ChiCTR2500103926, registered on May 9, 2025, PID: 275507.

Identifiers

PMID41402560
PMCPMC12804734

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.