Mild Cognitive Impairment: An Operational Definition and Its Conversion Rate to Alzheimer’s Disease
Bibliographic record
Abstract
OBJECTIVE: Because of discrepant findings regarding the accuracy of mild cognitive impairment (MCI) in predicting Alzheimer's disease (AD), further study of this construct and conversion rates is essential before use in clinical settings. We aimed to develop an operational definition of MCI consistent with criteria proposed by the Mayo Alzheimer's Disease Center, and to examine its conversion rate to AD. METHODS: Patients were identified from an inception cohort of patients with at least a 3-month history of memory problems, and referred to a 2-year university teaching hospital investigation by primary care physicians. We classified 161 nondemented patients at baseline using MCI criteria. Diagnostic workups were completed annually, and patients were classified as meeting criteria for AD or showing no evidence of dementia after 1 and 2 years. RESULTS: Of 161 patients, 35% met MCI criteria at baseline. Conversion rates to AD were 41% after 1 year, and 64% after 2 years. Logistic regression analyses to examine predictive accuracy of MCI after 1 and 2 years, with age and education as covariates, were significant (p < 0.0001). After 1 year, MCI showed an optimal sensitivity of 91% and specificity of 79%, and after 2 years, these values were 88 and 83%, respectively. CONCLUSIONS: MCI is an accurate predictor of AD over 1 and 2 years in patients referred by their primary care physicians. Discrepancies in conversion rates may be due to the manner in which patients are recruited to studies as well as the use of different measures to operationalize the construct.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".