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Record W2324737245 · doi:10.1097/wad.0000000000000044

Mild Cognitive Impairment Due to Alzheimer Disease is Less Likely Under the Age of 65

2014· article· en· W2324737245 on OpenAlexaff
Soojeong Shin, Jong Hun Kim, Jeong Hee Cho, Gyu Sik Kim, Sunah Choi, Jun Hong Lee

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsNeuroRx Research (Canada)
FundersNational Institute on AgingU.S. Department of Defense
KeywordsDementiaAlzheimer's diseaseDiseaseCognitive impairmentNeuroimagingMedicinePsychologyAudiologyCognitionInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Patients with amnestic mild cognitive impairment (aMCI) are considered to have a high risk for Alzheimer dementia (AD). Even high positive predictive values, however, cannot be guaranteed even by tests with high sensitivity and specificity when disease prevalence is low. If we regard the clinical criteria for aMCI as a test for predicting aMCI due to AD, the positive predictive value of the criteria will be low by definition in young patients with aMCI (age below 65 years) because of the low prevalence of AD in this age group. To test this hypothesis, we compared CSF biomarkers for AD between young (age below 65 years) and old (age 65 years or older) age groups of normal cognition, aMCI, and AD of the Alzheimer's Disease Neuroimaging Initiative database. Using these biomarkers, we observed that the prevalence of aMCI due to AD differed significantly between the young and the old. For example, only 28.2% young aMCI, but 63.2% old aMCI, had abnormal CSF amyloid measures consistent with AD pathology. As posited, the presence of aMCI due to AD was lower in young aMCI than in old aMCI. Given that the likelihood of aMCI due to AD is reduced in younger subjects, more attention to and evaluation of alternative diagnoses need to be considered in this group.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.312
Teacher spread0.283 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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