Evidence map›Paper›PMID 40401030›Full record

ArticleFrontiers in neurology2025

Predicting early functional outcomes in aneurysmal subarachnoid hemorrhage in endovascular coiling and surgical clipping.

Liang Chu, Kan Cao, Kuan Jiang, Yunpeng Lu, Ming Qi, Da Wu

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Liang ChuThe Affiliated Yixing Hospital of Jiangsu University, Yixing, China.
Kan CaoThe Affiliated Zhenjiang First Hospital of Jiangsu University, Zhenjiang, China.
Kuan JiangThe Affiliated Yixing Hospital of Jiangsu University, Yixing, China.
Yunpeng LuThe Affiliated Yixing Hospital of Jiangsu University, Yixing, China.
Ming QiThe Affiliated Yixing Hospital of Jiangsu University, Yixing, China.
Da WuThe Affiliated Yixing Hospital of Jiangsu University, Yixing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aimed to evaluate early functional outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) treated with either endovascular coiling or surgical clipping and to develop predictive models tailored to each treatment modality. Materials and methods: Patients diagnosed with aSAH were retrospectively enrolled from two hospitals in China between January 1, 2015, and December 31, 2022. Based on the treatment approach, patients were divided into two groups: endovascular coiling and surgical clipping. Independent risk factors were identified using least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression. The relative contribution of each significant factor was calculated, and nomograms were constructed accordingly. Model performance was subsequently assessed through validation analyses. Results: Multivariate analysis identified Hunt-Hess grade, Glasgow Coma Scale (GCS) score, modified Fisher Scale (mFS), D-dimer, age, and body temperature as independent predictors of early functional outcomes following endovascular coiling (all Conclusion: This study developed predictive nomogram models for early functional outcomes of aSAH patients undergoing endovascular coiling or surgical clipping treatments, emphasizing the importance of scoring systems and clinical parameters (such as D-dimer), demonstrating strong clinical utility.

Indexed as

aneurysmal subarachnoid hemorrhageD-dimerearly functional outcomesendovascular coilingnomogramsurgical clipping

Identifiers

PMID40401030
PMCPMC12092221

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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.