Risk Classification in Mild Cognitive Impairment Patients for Developing Alzheimer's Disease
Bibliographic record
Abstract
The objective of this study was to develop new risk classifications for conversion to Alzheimer's disease (AD) by comparing the relative reliability of classifiers in patients with mild cognitive impairment (MCI). The 397 MCI subjects and all baseline data, including characteristics, neuropsychological tests, cerebrospinal fluid biomarkers and MRI findings in Alzheimer's Disease Neuroimaging Initiative (ADNI), were used for analysis by Cox proportional hazard regression, bootstrap sampling, and c-index. Multivariate Cox regression analysis revealed the following factors to be associated with increased risk of conversion from MCI to AD during the 53-month follow-up period: AVLT 30-minute delayed recall, AVLT trial 1, Boston naming, logical delayed recall, trail-making B, CDR-sob, ADAS13, the cortical thickness of the right inferior temporal lobe (st91ta), and the left hippocampus volume. The combinations of ADAS13 at a cutoff point of 15.67 with CDR-sob at 1.5 or with the cortical thickness of the right inferior temporal lobe at 2.56 mm3 produced high conversion rates of 92.7% (82.4%-100.0%) and 88.8% (77.3%-100.0%), respectively, at 48 months. The discriminative ability based on c-index for the proposed combination was 0.68. The sample size was estimated as 504 in the group with a combination of ADAS13 and CDR-sob whose conversion rate is highest. The combination of ADAS13 with CDR-sob at an optimal cutoff point has a high reliability in classifying the MCI patients into high- and low-risk conversion to AD and will be benefit for patients' assessment and potentially facilitate the clinical development of novel therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".