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Record W4386845698 · doi:10.1002/trc2.12419

Could cerebrospinal fluid leak contribute to the link between traumatic brain injury and dementia?

2023· article· en· W4386845698 on OpenAlexaff
ZhiDi Deng, Esme Fuller‐Thomson

Bibliographic record

VenueAlzheimer s & Dementia Translational Research & Clinical Interventions · 2023
Typearticle
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsUniversity of TorontoInstitute for Work & HealthUniversity of Alberta
Fundersnot available
KeywordsDementiaMedicineTraumatic brain injuryCerebrospinal fluidPopulationInternal medicinePediatricsAnesthesiaPsychiatryDisease

Abstract

fetched live from OpenAlex

We read with interest the fascinating research conducted by Schievink and colleagues on the link between cerebrospinal fluid (CSF) leak and symptoms of behavioral variant of frontotemporal dementia (bvFTD), an early-onset dementia.1 This study found that CSF venous fistula, a type of spinal CSF leak, is found in ≈40% of their included patients with symptoms of bvFTD and spontaneous intracranial hypotension or brain sagging. Nine patients were found to have CSF venous fistula on imaging. Surgical ligation of the spinal CSF leaks resolved bvFTD symptoms in all of these nine patients. It was positive to see that, once identified, correction of CSF venous fistula could reverse such serious and devastating symptoms. Previous evidence suggests that patients with a history of traumatic brain injury (TBI) are at elevated risk of dementia diagnosis. For example, a population-based study in Taiwan found that, even after adjusting for factors such as socioeconomic status and presence of comorbidities, the risk of developing dementia of any type is 1.7-fold greater among patients with a history of TBI as compared to those without.2 This study further identified that the association between TBI and dementia was stronger in younger patients, thus indicating a potential link to early onset dementia.2 Similar findings were demonstrated by another study in Sweden. This 33-year follow-up study found that TBI was strongly associated with an increased risk of non-Alzheimer's types of early-onset dementia.3 The association was significant even after adjustment for covariates among those with only one mild TBI, as well as for those with two or more mild TBIs, and those with one severe TBI.3 The underlying mechanism between TBI and dementia is currently unclear. However, the findings of Schievink et al. provide insight into one potentially causal pathway to explain at least some of the observed association. Because CSF leaks are identified in ≈1% to 3% of all TBIs in adults,4 individuals with a history of TBI may thus be at risk of dementia symptoms and diagnosis partly due to spinal CSF leaks and intracranial hypotension. In individuals with a history of TBI, the presence of chronic and persistent headaches may suggest spinal CSF leaks and intracranial hypotension.5 Future study in individuals with dementia and a history of TBI may benefit from screening for the presence chronic headaches. If, in individuals with past TBI, higher odds of headache are found in individuals with dementia as compared to those without dementia, then further imaging studies in the select individuals with headaches could be conducted to assess for the presence of frontotemporal dementia brain sagging syndrome and CSF leaks, and thus possibly provide evidence for one of the links between TBI and dementia. This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. None of the following authors have any proprietary interests or conflicts of interest related to this submission. Author disclosures are available in the supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.407
GPT teacher head0.536
Teacher spread0.129 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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