Evidence map›Paper›PMID 41204188›Full record

ArticleBMC neurology2025

Evidence gap in predicting intracranial haemorrhage risk in people with glioma on anticoagulants: a scoping review.

Taofiq Adedeji Adeyemo, Sarah Greenley, Fiona Ware, Farzana Haque, Anthony Maraveyas, William Stephen Jones

Abstract readScoping Review
In one paragraph

Article in BMC neurology, 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

6 authors.

Taofiq Adedeji AdeyemoCentre of Excellence for Data Science, Artificial Intelligence and Modelling (DAIM), Faculty of Science and Engineering, University of Hull, Hull, UK. t.a.adeyemo-2021@hull.ac.uk.
Sarah GreenleyHull York Medical School, University of Hull, Cottingham Road, Hull, HU6 7RX, UK.
Fiona WareAcademic Library Services, University of Hull, Hull, UK.
Farzana HaqueHull York Medical School, University of Hull, Cottingham Road, Hull, HU6 7RX, UK.
Anthony MaraveyasHull York Medical School, University of Hull, Cottingham Road, Hull, HU6 7RX, UK.
William Stephen JonesCentre of Excellence for Data Science, Artificial Intelligence and Modelling (DAIM), Faculty of Science and Engineering, University of Hull, Hull, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeople with glioma (PwG), a type of brain tumour, have an elevated risk of developing venous thromboembolism (VTE). When VTE occurs, anticoagulant therapy is typically initiated, and in some cases, it may be prescribed prophylactically. However, these patients are also at risk of intracranial haemorrhage (ICH) as a complication of anticoagulation. Despite the clinical importance of this risk-benefit balance, it remains unclear whether predictive tools exist to guide anticoagulation decisions in this population.

methodsWe conducted a scoping review to determine whether predictive models exist for estimating the risk of intracranial haemorrhage (ICH) in people with glioma (PwG) receiving anticoagulant therapy. For any models identified, we assessed their methodological quality and predictive performance. Our search included MEDLINE, EMBASE (via Ovid), and the Cochrane Library, covering publications up to 29 November 2024. Studies were eligible if they employed predictive modelling to assess ICH risk in anticoagulated PwG. Two reviewers independently screened studies and extracted data. We used the PROBAST tool to evaluate model quality.

resultsOf the 1,585 articles screened, none met the inclusion criteria. Although some studies reported on ICH risk in PwG, none developed or validated predictive models tailored to this clinical context. One excluded study provides conceptual insights that may inform future modelling efforts.

conclusionsThe absence of these models underscores a critical gap in neuro-oncology research and highlights the urgent need for targeted model development to support anticoagulation decision-making in PwG.

Indexed as

AnticoagulantsBrain NeoplasmsGliomaIntracranial HemorrhagesVenous ThromboembolismEvidence GapsHumansRisk AssessmentAnticoagulantsAnticoagulantsGliomaIntracranial haemorrhagesPredictionPredictive model

Identifiers

PMID41204188
PMCPMC12595886

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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.