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Record W2165480541 · doi:10.1017/s1041610210001596

Does executive impairment define a frontal variant of Alzheimer's disease?

2010· article· en· W2165480541 on OpenAlexaff
Mark Woodward, Henry Brodaty, Karyn Boundy, David Ames, Greg Blanch, Robert Balshaw

Bibliographic record

VenueInternational Psychogeriatrics · 2010
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDiseaseExecutive dysfunctionPsychologyAlzheimer's diseaseExecutive summaryExecutive functionsCognitive impairmentNeuroscienceMedicineNeuropsychologyCognitionBiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: People with Alzheimer's disease (AD) who present with prominent frontal features such as a dysexecutive syndrome may be difficult to differentiate clinically from subjects with frontotemporal lobar degeneration (FTLD). This study was performed to improve the differential diagnosis between AD and FTLD and to better characterize the AD subgroup with greater executive dysfunction. METHODS: Using a well-defined prospectively studied cohort of cognitively impaired subjects, which included those with AD and with FTLD, we nominated a frontal variant of AD (FvAD) group as those AD subjects with the lowest quartile of scores on the Frontal Assessment Battery (FAB), indicating greatest executive dysfunction, and compared them with the rest of the AD cases (whom we called the AD group) and those with FTLD across several baseline variables including cognitive, functional and behavioral scales. We also compared the changes from baseline for these three groups at 6 and 12 months. Additionally, we controlled for dementia severity by matching AD and FTLD cases on a functional scale, the SMAF, and repeated the same comparisons with these severity-matched groups. RESULTS: The 114 FvAD subjects had a mean age of 78.1 years and Mini-mental State Examination (MMSE) scores of 16.6, and the (remaining) AD group had a mean age of 78.4 years and MMSE of 22.4. There were 30 FTLD subjects with a mean age at baseline of 70.9 years and a mean baseline MMSE of 23.4. The FvAD group was significantly more severely impaired than the other two groups on all baseline assessments except the behavioral scale, the Neuropsychiatric Inventory (NPI), where there was insignificantly less impairment than in the FTLD group. In the analysis of subjects matched at baseline for functional impairment, the FvAD and FTLD groups were not significantly different on most assessment scales although on the FAB, clock-drawing and MMSE the FvAD subjects were still significantly more impaired. These two severity-matched groups were also similar in other baseline characteristics except for older age and less psychotropic use in the FvAD group. The severity-matched FvAD group was significantly different from the AD group in almost all assessment scales. All three unmatched and matched groups declined similarly over 12 months. CONCLUSIONS: When groups were not matched for baseline severity, the use of the FAB defined a group of AD subjects with greater executive dysfunction that were distinguished from both the remainder of the AD and FTLD subjects in almost all domains except behavioral disturbance and probably were just more severely affected AD subjects. The FAB is thus more useful as a marker of dementia severity than as a scale to detect a frontal variant of AD or to distinguish AD from FTLD. Controlling for severity, however, did allow the definition of a subgroup of AD subjects that more closely resembled FTLD subjects than the remainder of the AD subjects. It is proposed that subjects with dementia presenting with greater executive impairment but without prominent behavioral symptoms are likely to have AD rather than FTLD, especially if they are quite functionally impaired. With time FTLD subjects develop increasing executive dysfunction and increasingly resemble the more severely affected AD subjects.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.011
GPT teacher head0.317
Teacher spread0.307 · 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 teacher head, not a consensus.

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

Citations37
Published2010
Admission routes1
Has abstractyes

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