SNIPE score can capture prodromal Alzheimer’s in cognitively normal subjects
Bibliographic record
Abstract
Abstract Capturing early changes in the brain related to Alzheimer’s disease may lead to models that successfully predict cognitive decline and the eventual onset of dementia, well ahead of onset of clinical symptoms. In this study we used both hippocampal volume and our hippocampal driven SNIPE score to show which marker better captures Alzheimer’s related changes in a large dataset of normal controls (N=515) from the ADNI study, comparing controls that remain cognitively stable and controls that progress to either MCI or Alzheimer’s dementia during 10 years of follow-up (median follow-up: 30 months). We measured hippocampal volume and SNIPE score and found that the effect size to differentiate between stable and progressor groups was significantly larger for SNIPE score than for volume. Our results also show that there is a significant age-related difference between groups for both markers, and the difference is greater with the SNIPE score. Our experiments show that considering high sensitivity of our SNIPE score regarding to early AD-related brain changes, this marker is a better candidate in comparison to hippocampal volume for predicting the future onset of dementia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".