Predicting Alzheimer's disease with practice effects, APOE genotype and brain metabolism
Bibliographic record
Abstract
After the paper Cognition or genetics. Predicting progression to Alzheimer's disease with practice effects, APOE genotype and brain metabolism [Neurobiol Aging, 2018; 71:234–240] was published, we identified a coding error of one of the variables analyzed. To correct, update and expand the previous work, we compared simple and complex regression-based Reliable Change Index (RCIRB) to analyze the risk of progression to AD (AD-risk) after six years using either delayed recall or recognition scores. Auditory Verbal Learning Test scores at six months for 394 individuals with normal cognition from the ADNI were used to build the regression. In 816 individuals with amnestic mild cognitive impairments, the AD-risk was associated with age, brain metabolism, APOE-ε4, recognition hits, the discrimination index, and low practice effects in the complex RCIRB only. The complex RCIRB outperformed the simple RCIRB. Small correlations were found between practice effects and both Aβ (highest r = 0.218) and TAU (highest r = -0.183). RCIRB are computationally simple and provide sensitive AD-risk estimates in combination with APOE-ε4 and FDG-PET.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".