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Record W2098918351 · doi:10.1016/j.jalz.2012.05.1171

O3‐06‐01: Chronic multimorbidity affects hippocampal volume in nondemented older individuals

2012· article· en· W2098918351 on OpenAlexaboutno aff
Grégoria Kalpouzos, Sara Angleman, Lars Bäckman, Laura Fratiglioni

Bibliographic record

VenueAlzheimer s & Dementia · 2012
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroimagingMedicineDementiaAtrophyPopulationMagnetic resonance imagingVoxelPittsburgh compound BDiseasePathologyPsychiatryRadiology

Abstract

fetched live from OpenAlex

Most neuroimaging protocols that have assessed brain atrophy in normal aging have used stringent criteria to include only people free from diseases (e.g., hypertension, diabetes). However, epidemiological studies have shown that the majority of the elderly population is affected by multiple diseases. Thus, previous neuroimaging findings do not represent the general aging population. Here we aimed to study the effect of morbidity (one chronic disease) and multimorbidity (co-occurrence of 2 or more chronic disorders) on the hippocampus (HC), whose structural integrity is vulnerable to aging and pathological conditions including Alzheimer's disease. Study participants were derived from the Swedish National study of Aging and Care in Kungsholmen (SNAC-K). From the 555 non-demented individuals who underwent a magnetic resonance imaging protocol (MRI), 442 were retained after exclusion due to technical issues, brain pathology (e.g., stroke), and diseases directly related to the brain (e.g., Parkinson's disease). Participants with MMSE < 27 were also excluded. Subjects were stratified by age: 60, 66, 72, and 78+. HC volume was determined on T1-weighted MRIs, which were preprocessed in SPM8, using the voxel-based morphometry approach (segmentation, DARTEL spatial normalization with customized template creation, smoothing). The gray-matter images were further affine-aligned to standard Montreal Neurological Institute (MNI) space, and voxel-based analyses were computed within the hippocampal region-of-interest derived from the AAL-MNI atlas. We compared subjects with no disease, morbidity, and multimorbidity in the whole sample and within age groups. In the whole sample, controlling for age, gender, and education, HC volume was reduced when subjects had more than 2 diseases, whereas no effect was detected in the presence of only one disease. This global pattern held within age groups after 66 years old, and was most pronounced amongst the 66-year-olds.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.032
GPT teacher head0.326
Teacher spread0.294 · 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
Published2012
Admission routes1
Has abstractyes

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