Prognosis of Early-Onset vs. Late-Onset Mild Cognitive Impairment: Comparison of Conversion Rates and Its Predictors
Bibliographic record
Abstract
Background: Despite having the same histopathological characteristics, early-onset and late-onset Alzheimer’s disease (AD) patients show some distinct clinical and neuropsychological profiles. Early Onset Mild Cognitive Impairment (EOMCI) is a less characterized group. The aim of this study is to characterize MCI probably due to AD in terms of the clinical, genetic, Cerebrospinal fluid (CSF) biomarkers profile and conversion rate of EOMCI, compared to the late-onset form (LOMCI). Methods: 159 MCI patients were divided in two groups: 52 EOMCI (onset < 65 years) and 107 LOMCI (onset ≥ 65 years). We investigated differences in neuropsychological scores, clinical variables, ApoE genotype, CSF biomarkers (Aβ42, t-Tau and p-Tau) in both groups. Conversion was ascertained during follow-up. Results: EOMCI showed a longer duration of symptoms prior to the first evaluation (EOMCI = 4.57 vs. LOMCI = 3.31, p = 0.008) and scored higher on the subjective memory complaints scale (9.91 vs. 7.85, p = 0.008), but performed better in brief cognitive tests (27.81 vs. 26.51, p < 0.001 in Mini-Mental State Examination; 19.84 vs. 18.67, p = 0.005 in Montreal Cognitive Assessment) than LOMCI. ApoE genotype distribution and CSF biomarker profile were similar in both groups, as was the conversion risk. Lower Aβ42 (Hazard ratio (HR): 0.998, 95% Confidence Interval (CI) = [0.996–1.000], p = 0.042), higher t-Tau levels (HR: 1.003, 95%CI = [1.000–1.005], p = 0.039) and higher scores in the Alzheimer Disease Assessment Scale-Cognitive (HR: 1.186, 95%CI = [1.083–1.299], p = 0.002) increased the risk of conversion. Discussion: Despite differences in memory performance and memory complaints, EOMCI and LOMCI seem to represent indistinct biological groups that do not have a higher risk of conversion to AD or differ in risk factors for conversion.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".