Cortical Thickness Analysis to Detect Progressive Mild Cognitive Impairment: A Reference to Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND/AIMS: Mild cognitive impairment (MCI) is associated with an increased risk of Alzheimer's disease (AD). It would be advantageous to be able to distinguish the characteristics of those MCI patients with a high probability to progress to AD if one wishes to monitor the disease development and treatment. METHODS: We assessed the baseline MRI and maximum of 7 years clinical follow-up data of 60 MCI subjects in order to examine differences in cortical thickness (CTH) between the progressive MCI (P-MCI) and stable MCI (S-MCI) subjects. CTH was measured using an automatic computational surface-based method. During the follow-up, 15 MCI subjects converted to AD on average 1.9 +/- 1.3 years after the baseline examination, while 45 MCI subjects remained stable. RESULTS: The P-MCI group displayed significantly reduced CTH bilaterally in the superior and middle frontal, superior, middle and inferior temporal, fusiform and parahippocampal regions as well as the cingulate and retrosplenial cortices and also in the right precuneal and paracentral regions compared to S-MCI subjects. CONCLUSIONS: Analysis of CTH could be used in conjunction with neuropsychological testing to identify those subjects with imminent conversion from MCI to AD several years before dementia diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".