Evidence map›Paper›PMID 38840600›Full record

ReviewSurgical neurology international2024

Meningioma recurrence: Time for an online prediction tool?

Abdulrahman Albakr, Amir Baghdadi, Brij S Karmur, Sanju Lama, Garnette R Sutherland

Abstract readReview
In one paragraph

Review in Surgical neurology international, 2024. 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. 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.

Abdulrahman AlbakrDepartment of Clinical Neurosciences, Project neuroArm, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
Amir BaghdadiDepartment of Clinical Neurosciences, Project neuroArm, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
Brij S KarmurDepartment of Clinical Neurosciences, Project neuroArm, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
Sanju LamaDepartment of Clinical Neurosciences, Project neuroArm, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
Garnette R SutherlandDepartment of Clinical Neurosciences, Project neuroArm, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Meningioma, the most common brain tumor, traditionally considered benign, has a relatively high risk of recurrence over a patient's lifespan. In addition, with the emergence of several clinical, radiological, and molecular variables, it is becoming evident that existing grading criteria, including Simpson's and World Health Organization classification, may not be sufficient or accurate. As web-based tools for widespread accessibility and usage become commonplace, such as those for gene identification or other cancers, it is timely for meningioma care to take advantage of evolving new markers to help advance patient care. Methods: A scoping review of the meningioma literature was undertaken using the MEDLINE and Embase databases. We reviewed original studies and review articles from September 2022 to December 2023 that provided the most updated information on the demographic, clinical, radiographic, histopathological, molecular genetics, and management of meningiomas in the adult population. Results: Our scoping review reveals a large body of meningioma literature that has evaluated the determinants for recurrence and aggressive tumor biology, including older age, female sex, genetic abnormalities such as telomerase reverse transcriptase promoter mutation, Conclusion: Although a challenging endeavor, a recurrence prediction tool for meningioma would provide critical information for the meningioma patient and the clinician making decisions on long-term surveillance and management of meningiomas.

Indexed as

Machine learningMeningiomaRecurrenceRecurrence risk toolRisk prediction tool

Identifiers

PMID38840600
PMCPMC11152515

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

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.