Evidence map›Paper›PMID 37900820›Full record

ArticleCase reports in oncology

A Single-Institution Retrospective Series of SARS-CoV-2 Infection in Adult Glioma Patients.

Tyler Lanman, Amber N Ruiz, Seema Nagpal

Open access · goldAbstract readCase Reports
In one paragraph

Article in Case reports in oncology. 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, top 53% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

3 authors at 1 institution in 1 country.

Tyler LanmanDepartment of Neurology, Stanford University, Stanford, CA, USA.
Amber N RuizDepartment of Neurology, Stanford University, Stanford, CA, USA.
Seema NagpalDepartment of Neurology, Stanford University, Stanford, CA, USA.
Stanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A subset of cancer patients is particularly vulnerable to SARS-CoV-2 infection; however, real-world outcomes-based data on primary central nervous system tumor patients is sparse. This retrospective series describes a cohort of adult glioma patients seen at Stanford Cancer Center between January 1, 2020, and June 30, 2022 who contracted SARS-CoV-2, which, to our knowledge, currently represents the largest single-institution comprehensive analysis of this patient population. We performed a retrospective search of patients seen in the Stanford Neuro-Oncology clinic, identifying 29 cases of COVID-19 amongst glioma patients and extracted clinical data via individual chart review. At the time of COVID-19 diagnosis, 15 patients had been vaccinated against SARS-CoV-2, 8 patients were taking dexamethasone, and 8 were undergoing cancer-specific treatment. Obesity, prior tobacco use, and diabetes were the most common comorbidities. Cough, sore throat, and congestion were the most common symptoms. Five patients were admitted to the hospital and two received COVID-19-specific treatment. None died from COVID-related causes or complications. Our data suggest that glioma patients seen at Stanford Cancer Center do not experience an exceptionally high COVID-19 infectivity, hospitalization, or mortality rate, especially when compared to other vulnerable populations such as lung cancer patients. High vaccination rates, adherence to COVID-19 guidelines, and low prevalence of comorbidities may have contributed to these results.

Indexed as

Brain tumorCOVID-19GlioblastomaGliomaPandemicSARS-CoV-2

Identifiers

PMID37900820
PMCPMC10601729
OpenAlexW4387542625

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.