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Record W3112932807 · doi:10.1002/alz.044067

Histopathological assessment and staging of large and small vessel disease associated with normal brain aging

2020· article· en· W3112932807 on OpenAlexaff
Caroline Dallaire‐Théroux, Stéphan Saïkali, Simon Duchesne

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsHôpital de l'Enfant-JésusUniversité Laval
Fundersnot available
KeywordsArteriolosclerosisNeuropathologyCerebral amyloid angiopathyMedicinePathologyDiseaseDementiaInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Vascular cognitive impairment is the manifestation of diffuse underlying cerebrovascular disease (CVD) that can be assessed at postmortem examination. However, CVD is nonspecific and therefore common in cognitively intact individuals, as prevalence increases with age. There is also an important overlap between CVD and other neurodegenerative conditions, especially Alzheimer’s disease, with more than 40% of cases being mixed aetiologies. We aimed to validate a histological scale able to assess the natural progression of small and large vessel lesions associated with normal brain aging, and further understand their potential contribution to cognitive impairment. Method Brain specimens from 63 cognitively intact participants aged 19 to 84 were examined and rated by two blinded and independent observers using a modified version of the Vascular Cognitive Impairment Neuropathology Guidelines (VCING; Skrobot et al. 2016). The scale focuses on nine anatomical regions and ten histological changes, including vascular wall lesions and secondary tissue damages. Weighted Kappa coefficients were calculated to estimate interrater reliability, and Spearman’s rank correlation test was used to calculate regional gradients of vascular load associated with age. Result Preliminary analyses suggest an inter‐observer agreement ranging from 0.39 to 1.00. Atherosclerosis, arteriolosclerosis, cerebral amyloid angiopathy and perivascular hemosiderin deposits were significantly correlated with age (ρ = 0.76, 0.74, 0.44 and 0.39, respectively; p < 0.005). There was also a trend in the severity of perivascular retraction and myelin loss associated with age (ρ = 0.26 and 0.31, p = 0.04 and 0.01). In arteriolosclerosis, the strongest regional gradients were observed in deeper brain structures (i.e., basal ganglia and thalamus). Conclusion These results suggest an existing cerebrovascular pathology that accumulates with normal aging, the burden of which can be reproducibly estimated with a modified version of the VCING scale. Our results can be used as normative benchmarks to assess the severity of occurrence. While large vessel pathology is a well‐known risk factor for vascular dementia, the contribution of small vessel disease to cognitive impairment has yet to be established. The use of a dedicated standardized histological staging scale is crucially needed to better understand its clinical implications and identify thresholds of pathological states associated with cognitive decline.

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)0.003
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.266
Teacher spread0.244 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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