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Record W2997281812 · doi:10.1161/strokeaha.119.025247

Blood-Brain Barrier Leakage

2019· article· en· W2997281812 on OpenAlexafffund
Yonatan Serlin, Jonathan Ofer, Gal Ben‐Arie, Ronel Veksler, Gal Ifergane, Ilan Shelef, Jeffrey Minuk, Anat Horev, Alon Friedman

Bibliographic record

VenueStroke · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsDalhousie UniversityJewish General HospitalMcGill University
FundersCanadian Institutes of Health Research
KeywordsMedicineBlood–brain barrierLeakage (economics)Internal medicineCentral nervous system

Abstract

fetched live from OpenAlex

Background and Purpose- The diagnosis of transient ischemic attack is challenging. Evidence of acute ischemia on MRI diffusion-weighted imaging is highly variable and confirmed in only about one-third of patients. This study investigated the significance of blood-brain barrier dysfunction (BBBD) mapping in patients with transient neurological deficits, as a diagnostic and prognostic biomarker required for risk stratification and stroke prevention. Methods- We used dynamic contrast-enhanced MRI to quantitatively map BBBD in a prospective cohort study of 57 patients diagnosed with transient ischemic attack/minor stroke and 50 healthy controls. Results- Brain volume with BBBD was significantly higher in patients compared with controls ( P=0.002). BBBD localization corresponded with the clinical presentation in 41 patients (72%) and was more extensive in patients with acute infarct on diffusion-weighted imaging ( P=0.05). Patients who developed new stroke during follow-up had a significantly greater BBBD at the initial presentation ( P=0.03) with a risk ratio of 5.35 for recurrent stroke. Conclusions- This is the first description of the extent and localization of BBBD in patients with transient ischemic attack/minor stroke. We propose BBBD mapping as a valuable tool for detection of subtle brain ischemia and a promising predictive biomarker required for risk stratification and stroke prevention.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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.

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

Citations47
Published2019
Admission routes2
Has abstractyes

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