Evidence map›Paper›PMID 41696750›Full record

ArticleFrontiers in neurology2025

Prognostic influences and prediction model construction for traumatic cervical spinal cord injury.

Xin Kang, Xiao-Bo Zhang, Xi-Dan Gao, Zi-Lin Gao, Zheng-Wei Xu, Yingang Zhang

Abstract read
In one paragraph

Article in Frontiers in neurology, 2025. 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

6 authors.

Xin Kang *Department of Orthopaedics, First Affiliated Hospital, Medical College of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Xiao-Bo Zhang *Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Xi-Dan GaoDepartment of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Zi-Lin GaoDepartment of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Zheng-Wei XuDepartment of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Yingang ZhangDepartment of Orthopaedics, First Affiliated Hospital, Medical College of Xi'an Jiaotong University, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Study design: Retrospective analysis of prognostic factors in traumatic cervical spinal cord injury (TCSCI) patients. Objective: Explore a novel approach for the prognosis assessment of TCSCI. Methods: A retrospective analysis of clinical data was conducted on patients who underwent operation at Xi'an Honghui Hospital between March 2016 and May 2023. The study included sex, age, hypertension, diabetes mellitus (DM), damage range, ossification of the posterior longitudinal ligament (OPLL), cervical spinal canal stenosis grading (CSCS), surgical timing, and postoperative complications (deep vein thrombosis, pulmonary complications, pressure ulcers, and urinary tract infections). A prognostic model was established by these differential factors and evaluating the sensitivity and specificity for predicting paralysis occurrence. Results: Paralysis was found to be 4.009 times more likely in patients with DM compared to those without (95% CI: 1.250-12.863, Conclusion: The prognosis of TCSCI is influenced by factors including DM, damage range, CSCS, surgical timing, and the number of postoperative complications. A clinical prognostic model was developed based on these prognostic factors, demonstrating a strong predictive capability for paralysis.

Indexed as

efficacy assessmentpredictive modelprognosisretrospective analysistraumatic cervical spinal cord injury

Identifiers

PMID41696750
PMCPMC12903919

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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.