Evidence map›Paper›PMID 39181692›Full record

ReviewAJNR. American journal of neuroradiology2025

Cortically Based Brain Tumors in Children: A Decision-Tree Approach in the Radiology Reading Room.

V Rameh, U Löbel, F D'Arco, A Bhatia, K Mankad, T Y Poussaint, C A Alves

Erratum issuedAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in AJNR. American journal of neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

7 authors.

V RamehFrom the Department of Radiology (V.R., T.Y.P., C.A.A.), Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts vanessa.rameh@childrens.harvard.edu.ORCID 0000-0002-6007-7216
U LöbelDepartment of Radiology (U.L., F.D., K.M.), Great Ormond Street Hospital for Children, National Health Service Foundation Trust, London, UK.ORCID 0000-0001-9844-3464
F D'ArcoDepartment of Radiology (U.L., F.D., K.M.), Great Ormond Street Hospital for Children, National Health Service Foundation Trust, London, UK.
A BhatiaDepartment of Radiology (A.B.), Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania.ORCID 0000-0002-3702-9304
K MankadDepartment of Radiology (U.L., F.D., K.M.), Great Ormond Street Hospital for Children, National Health Service Foundation Trust, London, UK.ORCID 0000-0001-5979-9337
T Y PoussaintFrom the Department of Radiology (V.R., T.Y.P., C.A.A.), Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0000-0002-0814-0791
C A AlvesFrom the Department of Radiology (V.R., T.Y.P., C.A.A.), Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts.ORCID 0000-0001-5877-9086

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cortically based brain tumors in children constitute a unique set of tumors with variably aggressive biologic behavior. Because radiologists play an integral role on the multidisciplinary medical team, a clinically useful and easy-to-follow flow chart for the differential diagnoses of these complex brain tumors is essential. This proposed algorithm tree provides the latest insights into the typical imaging characteristics and epidemiologic data that differentiate the tumor entities, taking into perspective the 2021 World Health Organization's classification and highlighting classic as well as newly identified pathologic subtypes by using current molecular understanding.

Indexed as

Brain NeoplasmsCerebral CortexDecision TreesAlgorithmsChildDiagnosis, DifferentialHumans

Identifiers

PMID39181692
PMCPMC11735440

What OpenQuestion holds

Textmetadata
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.