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Variations in case definition affect prevalence but not outcomes of mild cognitive impairment

2003· article· en· W2095173617 on OpenAlexaffabout
John D. Fisk, Heather Merry, Kenneth Rockwood

Bibliographic record

VenueNeurology · 2003
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDementiaActivities of daily livingAffect (linguistics)GerontologyMedicineNeuropsychologyPopulationCohortInstitutionalisationMemory impairmentCohort studyCognitionAlzheimer's diseaseDiseaseCognitive impairmentPsychologyPsychiatryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the prevalence estimates and 5-year outcomes of various case definitions of mild cognitive impairment (MCI). METHODS: The authors examined 1,790 adults 65 years of age or older who completed neuropsychological and clinical assessments in the Canadian Study of Health and Aging, a 5-year, representative, prospective cohort study. RESULTS: The most commonly used case definition of MCI yielded a population prevalence estimate of 1.03% (95% CI 0.66 to 1.40). Eliminating the requirements for subjective memory complaints and intact instrumental activities of daily living (IADL) increased the prevalence to 3.02% (CI 2.40 to 3.64). Five-year outcomes, including the risk of death, institutionalization, and dementia, were not distinctly different for various case definitions of MCI, but all were at increased risk of institutionalization (RR 2.3 to 5.2) and dementia (RR 9.3 to 19.7). Regardless of the case definition, most people with MCI developed dementia, chiefly Alzheimer disease (AD). Still, for each case definition, almost one third were considered to have no cognitive impairment after 5 years. CONCLUSIONS: Memory complaints and intact IADL may be unnecessary requirements for a case definition of MCI in population-based samples. The MCI criteria identify people at increased risk of AD, but the potential for improvement of a substantial proportion of those with MCI needs to be acknowledged.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.518

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)0.0000.000

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 head0.039
GPT teacher head0.336
Teacher spread0.297 · 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

Citations261
Published2003
Admission routes2
Has abstractyes

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