Llama3-SISE: An LLM-based Pipeline for Preclinical Staging of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, where subtle linguistic changes can emerge years before clinical symptoms. However, current large language models (LLMs) struggle to capture these weak signals due to their coherence bias, limiting their effectiveness for early AD detection. To address this challenge, we construct a new dataset, DementiaNet_Text, by transcribing and annotating patient interview recordings, and propose a Symptom-Informed Sequential Enhancement (SISE) pipeline. The pipeline first detects fine-grained linguistic anomalies, then disentangles them into structured symptom vectors, and finally integrates them into the Llama-3 embedding space to guide the model toward clinically relevant cues. Our goal is to improve classification of preclinical AD stages. Experiments show that the proposed Llama3-SISE achieves state-of-the-art results, with F1 gains of +4.8 in the Mid preclinical stage and +5.1 in the Late preclinical stage over the Meta-Llama-3-8B baseline, while maintaining competitive performance in Early and Health categories. In summary, our work demonstrates that explicitly modeling subtle linguistic anomalies provides a robust pathway for effective early AD screening.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".