The prevalence and biomarkers’ characteristic of rapidly progressive Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database
Bibliographic record
Abstract
Abstract Introduction The prevalence and detailed biomarkers’ characteristic of rapidly progressive Alzheimer's disease (rpAD) remain incompletely understood. Methods A total of 312 mild AD patients from the Alzheimer's Disease Neuroimaging Initiative database were chosen and dichotomized into rpAD and non‐rpAD groups. We performed the prevalence and comprehensive biomarker evaluation. Results The prevalence of rpAD was 17.6% in mild AD. Compared with non‐rpAD, there were no differences in APOE ε4/ε4, APOE ε3/ε4, and APOE ε2/ε4 genotype distribution, cerebrospinal fluid tau, phosphorylated tau (p‐tau), amyloid‐β, hippocampus volume, and amyloid deposition in rpAD. Yet, a lower p‐tau/tau ratio was observed in rpAD ( P = .04). rpAD showed region‐specific hypometabolism ([18F]fluorodeoxyglucose‐positron emission tomography [FDG‐PET]) ( P = .001). Receiver‐operating characteristic analysis of FDG‐PET demonstrated that left angular and left temporal cortices were the regions with higher area under the curve and predictive value for identifying clinical at‐risk rpAD. Discussion We identified that rpAD commonly existed in mild AD. Cerebral hypometabolism could provide potential clinical differential value for rpAD in the short‐term follow‐up period.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".