The diagnostic performance of neurofilament light chain in CSF and blood for Alzheimer's disease, frontotemporal dementia, and amyotrophic lateral sclerosis: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: A systematic review and meta-analysis was performed regarding the diagnostic performance of neurofilament light chain (NfL) in CSF and blood. METHODS: A database search was conducted for NfL biomarker studies in the context of Alzheimer's disease (AD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS) compared with controls (i.e., cognitively unimpaired, mild cognitive impairment, or disease mimics). RESULTS: In groups with a sufficient number of studies, the performance of NfL in blood and CSF was similar. Compared with disease mimics, we observed that CSF NfL had strong discriminatory power for ALS, modest discriminatory power for FTD, and no discriminatory power for AD. NfL provided the greatest separation between ALS and cognitively unimpaired controls in both the blood and CSF, followed by FTD (CSF and blood), then AD (blood and CSF). DISCUSSION: Comparable performance of CSF and blood NfL in many groups demonstrates the promise of NfL as a noninvasive biomarker of neurodegeneration; however, its utility in clinically meaningful scenarios requires greater scrutiny. Toward clinical implementation, a more comprehensive understanding of NfL concentrations in disease subtypes with overlapping phenotypes and at defined stages of disease, and the development of a harmonization program, are warranted.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".