Evidence map›Paper›PMID 39834963›Full record

ReviewCureus2024

Re-optimizing the Time Frame for Classifying Cerebral Venous Sinus Thrombosis: An Unmet Need.

Nikhil Vojjala, Supriya Peshin, Jayalekshmi Jayakumar, Nikhil Kotla, Adit Dharia, Mamtha Balla, Geetha Krishnamoorthy

Abstract readReview
In one paragraph

Review in Cureus, 2024. 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

7 authors.

Nikhil VojjalaInternal Medicine, Trinity Health Oakland Hospital, Pontiac, USA.
Supriya PeshinInternal Medicine, Norton Community Hospital, Norton, USA.
Jayalekshmi JayakumarInternal Medicine, The Brooklyn Hospital Center, New York, USA.
Nikhil KotlaInternal Medicine, Trinity Health Oakland Hospital, Pontiac, USA.
Adit DhariaInternal Medicine, HCA Florida Oak Hill Hospital, Florida, USA.
Mamtha BallaInfectious Disease and Transplant, MD Anderson Cancer Center, Houston, USA.
Geetha KrishnamoorthyInternal Medicine, Trinity Health Oakland Hospital, Pontiac, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cerebral venous thrombosis (CVT) is a rare and complex form of stroke, representing a small percentage of all stroke cases. The disease's clinical presentation is highly variable, involving a wide range of medical specialists due to its diverse manifestations. Over the past decade, significant advancements in understanding CVT have been made, particularly in light of the COVID-19 pandemic and subsequent vaccination efforts. Current classification systems of CVT, which categorize the disease as acute, subacute, or chronic based on symptom onset, vary significantly in their criteria and practical relevance. Key challenges include aligning these time-based classifications with real-world delays in diagnosis and treatment, as well as understanding the timing and nature of complications such as raised intracranial pressure and the need for decompressive procedures. Radiological studies of clot morphology provide additional insights, suggesting that clot characteristics may indicate the recanalization potential and influence treatment strategies. However, the relationship between clot recanalization and clinical outcomes remains debated. A comprehensive classification that integrates clinical, radiological, and prognostic parameters could aid in better managing CVT and improving patient outcomes.

Indexed as

cerebral venous sinus thrombosis (cvst)cerebral venous thrombosis cvtcovid 19iscvt2re-spect

Identifiers

PMID39834963
PMCPMC11742759

What OpenQuestion holds

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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.