MétaCan
Menu
Back to cohort
Record W2255906570 · doi:10.1161/str.46.suppl_1.tmp109

Abstract T MP109: Leptomeningeal Collaterals, Ageing and Metabolic Syndrome in Development of White Matter Hyperintensities

2015· article· en· W2255906570 on OpenAlexaff
Ondřej Volný, Vivek Nambiar, Sung‐Il Sohn, James E. Faber, Donald G. Welsh, Toloupe Sajobi, Robert Mikulík, Andrew M. Demchuk, Bijoy K. Menon

Bibliographic record

VenueStroke · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineHyperintensityWhite matterStroke (engine)Fluid-attenuated inversion recoveryCardiologyMiddle cerebral arteryInternal medicineRadiologyNuclear medicineMagnetic resonance imagingIschemia

Abstract

fetched live from OpenAlex

Background: Ageing and metabolic syndrome are associated with poor leptomeningeal collateral status. Animal studies suggest that collateral rarefaction and consequent decrease in vascular efficiency may result in increase in white matter hyperintensities. Using mediational analysis, we test if the known effect of ageing and metabolic syndrome on development of white matter hyperintensities is mediated through collateral status. Methods: Data are from the Keimyung Stroke Registry. Consecutive patients with M1 segment middle cerebral artery (MCA) ± intracranial internal carotid artery (ICA) occlusions on baseline CT-angiography (CTA) and brain MRI done within 90 minutes after admission CT/CTA, from May 2004 to July 2009, were included. Baseline and follow-up imaging was analyzed blinded to all clinical information. Two raters assessed leptomeningeal collaterals on baseline CTA by consensus, using previously validated regional leptomeningeal score (rLMC). FLAIR volume of white matter hyperintensities (ml) was measured in the unaffected hemisphere using Quantomo® software. The template of Baron and Kenney along with two tests (Sobel’s and Aroian’s) was used to test for the presence of mediation. Results: Baseline characteristics (n=120): mean age 67.4±11.4 years, male (53.3%), median baseline NIHSS 14 (IQR 11-20), and median stroke symptom onset to CTA 166 minutes (IQR 96-262). Poor collateral status at baseline (rLMC score 0-10) was seen in 42/120 (35%). Mean periventricular hyperintensity (PVH) volume was 6.5 ml (SD=6.0) while mean white matter hyperintensity (WMH-total) volume was 8.6 ml (SD=8.0). Higher age was associated with increased PVH and WMH-total (p<0.01) while metabolic syndrome was associated with increased PVH only (p=0.03). We did not find statistical evidence of leptomeningeal collaterals mediating the association between ageing and PVH/WMH-total or between metabolic syndrome and PVH (Sobel’s and Aroian’s test p>0.05). Conclusion: The effect of ageing and metabolic syndrome on development of white matter hyperintensities is independent of an effect mediated through the poor collateral status.

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)0.003
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 score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.001

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 head0.043
GPT teacher head0.257
Teacher spread0.214 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueStrokeSame topicNeurological Disease Mechanisms and TreatmentsFrench-language works237,207