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Record W2562565866 · doi:10.1093/brain/aww312

Increased heart rate and energy expenditure in frontotemporal dementia

2016· letter· en· W2562565866 on OpenAlexaff
Elizabeth Finger

Bibliographic record

VenueBrain · 2016
Typeletter
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsFrontotemporal dementiaAmyotrophic lateral sclerosisDementiaPsychologyOverweightDiseaseBody mass indexBasal metabolic rateMedicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

This scientific commentary refers to ‘Energy expenditure in frontotemporal dementia: a behavioural and imaging study’ by Ahmed et al . (doi:10.1093/aww263) . The obesity paradox, whereby being overweight or obese during mid-life is associated with higher rates of dementia in later life, while low body mass index (BMI) in older populations is associated with a higher risk of dementia, has been demonstrated in multiple studies of patients with Alzheimer’s disease (Fitzpatrick et al. , 2009). In another neurodegenerative disorder, frontotemporal dementia (FTD), classic descriptions suggest a different pattern, specifically weight gain with disease onset due to hyperphagia and increased sweet intake. However, BMI has not been found to correlate with food intake in FTD, raising the possibility of altered metabolism in patients with FTD (Ahmed et al. , 2016). In this issue of Brain , Ahmed et al. test this hypothesis by measuring activity levels and heart rate to characterize energy expenditure in patients with FTD (Ahmed et al. , 2016). They conclude that resting and total energy expenditure are increased in FTD, suggesting that the basal metabolic rate in patients with FTD may be altered as a part of the disease. A relationship between BMI, metabolism and several neurodegenerative disorders including Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis is now generally established, though the complex pathways mediating these associations …

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.119
Threshold uncertainty score0.869

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)0.0010.000

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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2016
Admission routes1
Has abstractyes

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