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Record W2744256473 · doi:10.1161/str.47.suppl_1.219

Abstract 219: Tumor Necrosis Factor Receptor 1 and Subclinical Cerebrovascular Disease: the Levels of Inflammatory Markers in Treatment of Stroke Study

2016· article· en· W2744256473 on OpenAlexaff
Mitchell S.V. Elkind, Leslie A. McClure, Jorge M. Luna, Óscar H. Del Brutto, Aleksandra Pikula, Oscar Benavente

Bibliographic record

VenueStroke · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsMedicineLacunar strokeStroke (engine)Internal medicineSubclinical infectionTumor necrosis factor receptor 1BiomarkerOdds ratioCardiologyRisk factorQuartileConfidence intervalGastroenterologyPathologyTumor necrosis factor alphaIschemic strokeIschemia

Abstract

fetched live from OpenAlex

Background: The role of inflammation in cerebral small vessel disease remains uncertain. Tumor necrosis factor-alpha receptor 1 (TNFR1) has been associated with atherosclerosis and risk of stroke. We hypothesized that TNFR1 concentrations would be associated with cerebral white matter disease (WMD) and subclinical infarcts in patients with recent lacunar stroke. Methods: Levels of Inflammatory Markers in the Treatment of Stroke (LIMITS) was an international, multicenter, ancillary biomarker study nested within the Secondary Prevention of Small Subcortical Strokes trial (SPS3; www.clinicaltrials.gov unique identifier: NCT00059306), a Phase III trial in patients with recent lacunar stroke. Patients had blood samples collected at enrollment, and concentrations of inflammatory biomarkers, including TNFR1, were measured using ELISA at a central laboratory. Enrollment MRI scans were read centrally and interpreted for silent infarcts and burden of WMD using semi-quantitative scales. We compared proportions of patients with prior infarcts and WMD scores across quartiles of TNFR1, and used logistic regression to estimate odds ratios and 95% confidence intervals (OR, 95%CI) to examine the relationship between TNFR1 and subclinical disease after adjusting for demographics and comorbidities. Results: Among 1004 lacunar stroke patients with TNFR1 data (mean age 63.3 ± 10.8 years), 407 (40%) had infarcts besides the qualifying infarct; half the cohort had 0-4 white matter lesions, 27% had 5-8 lesions, and 23% had >=9 lesions. TNFR1 levels were associated with WMD score >=9 (OR per standard deviation (SD) TNFR1=1.2, 95%CI 1.0-1.4). Larger proportions of those in the top quartile of TNFR1, compared to those in the lowest, had ≥9 white matter lesions (29% versus 19%, p=0.026) and additional infarcts (45% versus 37%, p=0.28). After adjusting for demographics and comorbidities, the effect of TNFR1 on WMD score >=9 (adjusted OR per SD=1.1, 95%CI 1.0-1.3) and additional infarcts (adjusted OR 1.2, 95%CI 1.0-1.3) attenuated. Conclusions: Among recent lacunar stroke patients, TNFR1 concentrations were associated with prior or subclinical cerebrovascular disease. Future studies of TNF and TNF inhibitors in cerebral ischemic disease may be warranted.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.286
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2016
Admission routes1
Has abstractyes

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