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Record W2605672047 · doi:10.1017/s1041610217000527

Prevalence, neurobiology, and treatments for apathy in prodromal dementia

2017· review· en· W2605672047 on OpenAlexafffund
Chelsea Sherman, Celina S. Liu, Nathan Herrmann, Krista L. Lanctôt

Bibliographic record

VenueInternational Psychogeriatrics · 2017
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
FundersU.S. National Library of MedicineNational Institute on AgingCanadian Institutes of Health Research
KeywordsApathyDementiaPsycINFOPsychiatryPsychologyClinical psychologyRisperidoneMEDLINEGalantaminePsychological interventionMedicineCognitionDiseaseSchizophrenia (object-oriented programming)Internal medicineDonepezil

Abstract

fetched live from OpenAlex

BACKGROUND: Apathy, characterized by diminished motivation, is a highly prevalent neuropsychiatric symptom in dementia. However, there is a substantial knowledge gap with regard to prevalence rates, neurobiological underpinnings, and effective treatments for apathy in pre-dementia states, including mild cognitive impairment (MCI) and mild behavioral impairment (MBI). METHODS: We conducted a comprehensive literature search using MEDLINE, Embase, and PsycINFO databases to identify available research on apathy in prodromal dementia. RESULTS: Apathy has consistently been detected in individuals with MCI with varying prevalence rates, and only recently has literature discussed the prevalence of apathy in MBI. Few pharmacological treatments have been utilized for apathy, with galantamine and risperidone showing mild reductions in apathetic behaviors. Non-pharmacological interventions in prodromal dementia are beginning to be explored and show promise, but few studies have replicated those results. DISCUSSION: More comprehensive guidelines for diagnosing apathy and further research investigating neurobiological mechanisms of apathy in MCI and MBI are required in order to effectively treat apathetic patients in prodromal dementia.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.085
GPT teacher head0.456
Teacher spread0.371 · 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
GenreReview

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

Citations61
Published2017
Admission routes2
Has abstractyes

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