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Record W2061984965 · doi:10.1155/2012/209570

Effects of Anosognosia on Perceived Stress and Cortisol Levels in Alzheimer’s Disease

2012· article· en· W2061984965 on OpenAlexafffund
Geneviève Arsenault‐Lapierre, Victor Whitehead, Sonia Lupien, Howard Chertkow

Bibliographic record

VenueInternational Journal of Alzheimer s Disease · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsInstitut universitaire en santé mentale de MontréalJewish General Hospital
FundersPfizer CanadaH. Lundbeck A/SAlzheimer Society Research ProgramCanadian Institutes of Health ResearchLundbeck CanadaAlzheimer SocietyPfizer
KeywordsAnosognosiaMedicineDiseaseStress (linguistics)Clinical psychologyPsychiatryGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Anosognosia, or unawareness of one's own cognitive deficits, may cause issues when measuring perceived stress and cortisol levels in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). The goal of this study was to examine the effects of anosognosia on perceived stress and salivary cortisol levels in normal elderly (NE) adults, MCI individuals, newly diagnosed AD patients, and long-lasting AD patients, suspected to show more anosognosia. An anosognosia index for perceived stress was computed by subtracting the score on the Perceived Stress Scale measured in the participants and their relative. Cortisol levels were measured four times a day over two nonconsecutive days. Greater anosognosia for dementia correlated with greater anosognosia for perceived stress in the group as a whole. However, no correlation between cortisol levels and either anosognosia for dementia or perceived stress was observed. Our results suggest that measuring perceived stress in AD patients may be influenced by anosognosia.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.317
Teacher spread0.270 · 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

Citations19
Published2012
Admission routes2
Has abstractyes

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