Can Clinical Data Predict Progression to Dementia in Amnestic Mild Cognitive Impairment?
Bibliographic record
Abstract
BACKGROUND: To determine whether clinical data obtained by history and physical examination can predict eventual progression to dementia in a cohort of elderly people with mild cognitive impairment. METHODS: A prospective, longitudinal study of a cohort of elderly subjects with amnestic Mild Cognitive Impairment (MCI). Ninety subjects meeting the criteria for amnestic MCI were recruited and followed annually for an average of 3.3 years. Main outcome measure was the development of dementia determined by clinical assessment with confirmatory neuropsychological evaluation. RESULTS: Fifty patients (56%) developed dementia on follow-up. They were older, had lower Mini-mental status exam (MMSE) scores and a shorter duration of symptoms at the time of first assessment. Multivariate logistic regression analysis identified age at symptom onset as the only clinical parameter which distinguished the group that deteriorated to dementia from the group that did not. The odds ratio for age was 1.1 (confidence interval 1.04 - 1.18). CONCLUSIONS: Patients presenting with amnestic MCI insufficient for the diagnosis of dementia are at high risk of developing dementia on follow-up. In our cohort, 56% were diagnosed with dementia over an average period of 5.9 years from symptom onset. The only clinical predictor for the eventual development of dementia was older age at symptom onset. Clinical features alone were insufficient to predict development of dementia.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".