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Record W577743257 · doi:10.1016/j.jalz.2015.05.017

Neuropsychiatric symptoms as early manifestations of emergent dementia: Provisional diagnostic criteria for mild behavioral impairment

2015· article· en· W577743257 on OpenAlexaff
Zahinoor Ismail, Eric E. Smith, Yonas E. Geda, David L. Sultzer, Henry Brodaty, Gwenn S. Smith, Luis Agüera-Ortíz, R. D. Sweet, David S. Miller, Constantine G. Lyketsos

Bibliographic record

VenueAlzheimer s & Dementia · 2015
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Institute on AgingNational Institutes of HealthNational Institute of Mental HealthJohns Hopkins UniversityU.S. Department of Veterans Affairs
KeywordsDementiaCognitive impairmentCognitionConstruct (python library)Clinical psychologyPsychologyCognitive declinePsychiatryMedicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Neuropsychiatric symptoms (NPS) are common in dementia and in predementia syndromes such as mild cognitive impairment (MCI). NPS in MCI confer a greater risk for conversion to dementia in comparison to MCI patients without NPS. NPS in older adults with normal cognition also confers a greater risk of cognitive decline in comparison to older adults without NPS. Mild behavioral impairment (MBI) has been proposed as a diagnostic construct aimed to identify patients with an increased risk of developing dementia, but who may or may not have cognitive symptoms. We propose criteria that include MCI in the MBI framework, in contrast to prior definitions of MBI. Although MBI and MCI can co-occur, we suggest that they are different and that both portend a higher risk of dementia. These MBI criteria extend the previous literature in this area and will serve as a template for validation of the MBI construct from epidemiologic, neurobiological, treatment, and prevention perspectives.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.054
GPT teacher head0.373
Teacher spread0.319 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations839
Published2015
Admission routes1
Has abstractyes

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