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Record W3113230730 · doi:10.1002/alz.041324

The etiology of long‐term stable mild cognitive impairment

2020· article· en· W3113230730 on OpenAlexaff
Manu J. Sharma, Brandy L. Callahan

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEtiologyDementiaDepression (economics)MedicineCognitive impairmentInternal medicineGeriatric Depression ScaleMoodCognitive declineCognitionPsychosisPsychologyPsychiatryDiseaseDepressive symptoms

Abstract

fetched live from OpenAlex

Abstract Background Of those diagnosed with mild cognitive impairment (MCI), 5‐10% convert annually to dementia (Mitchell & Shiri‐Feshki, 2009). However, 5‐30% can remain cognitively stable (sMCI) for up to 10 years (Alves et al., 2018), suggesting a non‐neurodegenerative etiology in these patients. This study aims to examine the etiological underpinnings of those who remain cognitively stable versus those who decline over time. Method Participants from the National Alzheimer’s Coordinating Center were selected if they were nondemented at baseline, had available clinical data ≥5 years following baseline, and had available autopsy data <1.5 years following last visit. They were defined as stable normal controls (sNC; n = 106) or sMCI (n = 28) if cognitive status was unchanged between first and last visit (≥5 years later); declined normal controls (dNC; n = 128) if cognitive status at first visit was normal and MCI or demented by last visit; or declined MCI (dMCI; n = 139) if cognitive status was MCI at first visit and demented by last visit. Neuropathological features were dichotomized to reflect clinically significant levels (≥moderate neuritic plaque density; Braak neurofibrillary degeneration stage ≥4; Thal amyloid plaque phase ≥3; any Lewy bodies; ≥1 large arterial infarct; ≥1 lacune; ≥1 microbleed; ≥1 hemorrhage). Baseline psychiatric symptoms were quantified using the Geriatric Depression Scale (GDS; ≥6 considered clinically significant) and the Neuropsychiatric Inventory Questionnaire (NPI‐Q; grouped into ‘mood’, ‘agitation’ and ‘psychosis’ factors based on previous factor analysis: Aalten et al., 2003, Siafarikas et al., 2018). The groups were compared on frequencies of neuropathological features at autopsy and baseline psychiatric symptoms using chi‐square (χ 2). Result Demographics are presented in Table 1, with main analysis results in Table 2. Relative to other groups, sMCI had significantly fewer amyloid plaques (χ 2 = 44.2, p < 0.001) and more microinfarcts (χ 2 = 9.9, p = .019). GDS and NPI‐Q showed no significant relationship with sMCI. Conclusion Previous research indicates microinfarcts are independent of neurodegenerative disease (Launer et al., 2011). In line with this, our results suggest that sMCI may be caused by microinfarcts, rather than frank neurodegenerative disease. This study was limited as only neuropsychiatric symptoms were reported, and future studies should examine sMCI in relation to diagnosed psychiatric disorders.

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)0.002
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.043
GPT teacher head0.327
Teacher spread0.284 · 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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