The CentiMarker project: Standardizing quantitative Alzheimer's disease fluid biomarkers for biologic interpretation
Bibliographic record
Abstract
INTRODUCTION: Biomarkers play a crucial role in understanding Alzheimer's disease (AD) pathogenesis and treatment effects. However, comparing biomarker measures without standardization and appreciating their magnitude relative to the disease can be challenging. METHODS: To address this issue, we propose the CentiMarker approach, similar to Centiloid, which provides a standardized scale between normal (0) and nearly maximum abnormal AD (100) ranges. We applied this scale to cerebrospinal fluid (CSF) biomarkers in dominantly inherited AD and sporadic AD cohorts. RESULTS: CentiMarkers facilitated the interpretation of disease abnormality, demonstrating comparable changes and distributions of AD biomarkers across disease stages. CentiMarkers make the treatment effect more comparable than their original scales across various biomarkers. DISCUSSION: The versatility of CentiMarkers makes it a valuable tool for standardized biomarker comparison in AD research, enabling informed cross-study comparisons and contributing to accelerated therapeutic development. Adoption of the CentiMarker scale could enhance biomarker reporting and advance our understanding of AD. HIGHLIGHTS: Comparing fluid biomarkers without appreciating their magnitude relative to the disease can be challenging. We propose a CentiMarker metric to standardize biomarker measures from normal (0) and nearly maximum abnormal AD (100) ranges. CentiMarkers make the treatment effect more comparable across various biomarkers than when using the original scales.
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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 | 0.072 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".