Evidence map›Paper›PMID 32922876›Full record

ArticleChinese neurosurgical journal2018

Pediatric brain arteriovenous malformation unfavorable hemorrhage risk: extrapolation to a morphologic model.

Zongze Li, Li Ma, Chunxue Wu, Jun Ma, Xiaolin Chen

Open access · diamondAbstract read
In one paragraph

Article in Chinese neurosurgical journal, 2018. 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, top 91% of its field
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, 2 citations in OpenAlex.

  1. Article
  2. 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

5 authors at 2 institutions in 1 country.

Zongze LiDepartment of Neurosurgery, Peking University International Hospital, Beijing, 102206 People's Republic of China.
Li MaDepartment of Neurosurgery, Peking University International Hospital, Beijing, 102206 People's Republic of China.ORCID 0000-0003-0894-7867
Chunxue WuDepartment of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Jun MaDepartment of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Xiaolin ChenDepartment of Neurosurgery, Peking University International Hospital, Beijing, 102206 People's Republic of China.
Capital Medical University · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChildren with brain arteriovenous malformations (bAVM) are at risk of life-threatening hemorrhage contributing to unfavorable neurological deficit in their early lives. Our aim was to propose a classification system predicting the unfavorable hemorrhage in children with bAVM.

methodsWe identified all consecutive children admitted to our institution for bAVMs between July 2009 and August 2015. A hemorrhage event was defined as unfavorable when it is life-threatening (requiring emergent invasive intervention) or with post-hemorrhage mRS > 3. The effects of demographic characteristics and bAVM morphology on unfavorable hemorrhage risk were studied using univariate and multivariable regression analyses, followed by discrimination analysis using area under the receiver operating curve (AUROC) and 5-fold cross validation.

resultsA total of 162 pediatric bAVM cases were identified, unfavorable hemorrhage occurred in 49 (30.2%). Periventricular nidus location (HR, 4.46; 95%CI, 1.93-10.31;

conclusionA morphologic model based on nidus location and venous drainage might predict unfavorable hemorrhage in children with bAVM.

Indexed as

Arteriovenous malformationChildrenHemorrhageRisk prediction

Identifiers

PMID32922876
PMCPMC7398325
OpenAlexW2808578780

What OpenQuestion holds

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LicenceCC BY
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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.