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Record W1504263151 · doi:10.1159/000430807

Predictors of Clinical Progression of Subjective Memory Impairment in Elderly Subjects: Data from the Clinical Research Centers for Dementia of South Korea (CREDOS)

2015· article· en· W1504263151 on OpenAlexfundno aff
Yun Jeong Hong, Bora Yoon, Yong S. Shim, Seon‐Ok Kim, Hwa Jung Kim, Seong Hye Choi, Jee Hyang Jeong, Soo Jin Yoon, Dong Won Yang, Jae‐Hong Lee

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders · 2015
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersAGE-WELL
KeywordsDementiaRecallClinical Dementia RatingPsychologyAlzheimer's diseaseCognitionCohortCohort studyMemory clinicMini–Mental State ExaminationMedicineGerontologyDiseaseInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: The aims of this study were to determine baseline factors related to the progression of subjective memory impairment (SMI) in elderly subjects and to develop a new modeling scale to predict progression. METHODS: Elderly subjects with SMI were recruited from the nationwide Clinical Research Centers for Dementia of South Korea (CREDOS) multicenter cohort and divided into two groups: (1) progressed to mild cognitive impairment or Alzheimer's disease or (2) stable without progression. Baseline clinical characteristics were compared between the groups, and the most relevant predictors of progression were assessed. A new modeling scale combining the predictors was developed. RESULTS: In total, 129 subjects with SMI were analyzed. The follow-up duration was 0.5-4.7 years, and the median time to event was 3.64 years. The progressing group (n = 29) differed from the stable group (n = 100) in terms of baseline age, apolipoprotein E4 (APOE4) status, and some cognitive domains. Older age, a lower Mini-Mental State Examination recall score, APOE4 carrier, and a lower verbal delayed recall score were the most relevant predictors of progression, and a new modeling scale with these 4 predictors provided a better explanation of progression. CONCLUSION: SMI subjects with a higher risk of progression can be identified using a new modeling scale and might need further evaluations and more frequent follow-up.

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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.049
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.112
GPT teacher head0.445
Teacher spread0.334 · 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 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

Citations35
Published2015
Admission routes1
Has abstractyes

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