Biomarker‐based stability in limbic‐predominant amnestic mild cognitive impairment
Bibliographic record
Abstract
Abstract Background The amnestic presentation of mild cognitive impairment (aMCI) represents the most common prodromal stage of Alzheimer's disease (AD) dementia. There is, however, some evidence of aMCI with typical amnestic syndrome but showing long‐term clinical stability. The ability to predict stability or progression to dementia in the aMCI condition is important, particularly for the selection of candidates in clinical trials. We aimed to establish the role of in vivo biomarkers, as assessed by cerebrospinal fluid (CSF) measures and [ 18 F]fluorodeoxyglucose (FDG)‐positron emission tomography (PET) imaging, in predicting prognosis in a large aMCI cohort. Methods We conducted a retrospective study, including 142 aMCI subjects who had a long follow‐up (4–19 years), baseline CSF data and [ 18 F]FDG‐PET scans individually assessed by validated voxel‐based procedures, classifying subjects into either limbic‐predominant or AD‐like hypometabolism patterns. Results The two aMCI cohorts were clinically comparable at baseline. At follow‐up, the aMCI group with a limbic‐predominant [ 18 F]FDG‐PET pattern showed clinical stability over a very long follow‐up (8.20 ± 3.30 years), no decline in Mini‐Mental State Examination score, and only 7% conversion to dementia. Conversely, the aMCI group with an AD‐like [ 18 F]FDG‐PET pattern had a high rate of dementia progression (86%) over a shorter follow‐up (6.47 ± 2.07 years). Individual [ 18 F]FDG‐PET hypometabolism patterns predicted stability or conversion with high accuracy (area under the curve = 0.89), sensitivity (0.90) and specificity (0.89). In the limbic‐predominant aMCI cohort, CSF biomarkers showed large variability and no prognostic value. Conclusions In a large series of clinically comparable subjects with aMCI at baseline, the specific [ 18 F]FDG‐PET limbic‐predominant hypometabolism pattern was associated with clinical stability, making progression to AD very unlikely. The identification of a biomarker‐based benign course in aMCI subjects has important implications for prognosis and in planning clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".