Evidence map›Paper›PMID 38137157›Full record

ReviewBrain sciences2023

Polypathologies and Animal Models of Traumatic Brain Injury.

Erin Freeman-Jones, William H Miller, Lorraine M Work, Josie L Fullerton

Abstract readReview
In one paragraph

Review in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. 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

4 authors.

Erin Freeman-JonesSchool of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow G12 8QQ, UK.
William H MillerSchool of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow G12 8QQ, UK.
Lorraine M WorkSchool of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow G12 8TA, UK.
Josie L FullertonSchool of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow G12 8TA, UK.ORCID 0000-0002-1967-9522

Funding

British Heart Foundation PG/21/10559
6 · The paper itself

Abstract

Traumatic brain injury (TBI) is an important health issue for the worldwide population, as it causes long-term pathological consequences for a diverse group of individuals. We are yet to fully elucidate the significance of TBI polypathologies, such as neuroinflammation and tau hyperphosphorylation, and their contribution to the development of chronic traumatic encephalopathy (CTE) and other neurological conditions. To advance our understanding of TBI, it is necessary to replicate TBI in preclinical models. Commonly used animal models include the weight drop model; these methods model human TBI in various ways and in different animal species. However, animal models have not demonstrated their clinical utility for identifying therapeutic interventions. Many interventions that were successful in improving outcomes for animal models did not translate into clinical benefit for patients. It is important to review current animal models and discuss their strengths and limitations within a TBI context. Modelling human TBI in animals encounters numerous challenges, yet despite these barriers, the TBI research community is working to overcome these difficulties. Developments include advances in biomarkers, standardising, and refining existing models. This progress will improve our ability to model TBI in animals and, therefore, enhance our understanding of TBI and, potentially, how to treat it.

Indexed as

animal modelspolypathologytraumatic brain injury

Identifiers

PMID38137157
PMCPMC10741988

What OpenQuestion holds

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LicenceCC BY
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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.