Neurofilament Light Chain as a Biomarker for Cognitive Decline in Parkinson Disease
Bibliographic record
Abstract
BACKGROUND: Neurofilament light chain protein (NfL) is a promising biomarker of neurodegeneration. OBJECTIVES: To determine whether plasma and CSF NfL (1) associate with motor or cognitive status in Parkinson's disease (PD) and (2) predict future motor or cognitive decline in PD. METHODS: Six hundred and fifteen participants with neurodegenerative diseases, including 152 PD and 200 healthy control participants, provided a plasma and/or cerebrospinal fluid (CSF) NfL sample. Diagnostic groups were compared using the Kruskal-Wallis rank test. Within PD, cross-sectional associations between NfL and Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) and Mattis Dementia Rating Scale (DRS-2) scores were assessed by linear regression; longitudinal analyses were performed using linear mixed-effects models and Cox regression. RESULTS: Plasma and CSF NfL levels correlated substantially (Spearman r = 0.64, P < 0.001); NfL was highest in neurocognitive disorders. PD participants with high plasma NfL were more likely to develop incident cognitive impairment (HR 5.34, P = 0.005). CONCLUSIONS: Plasma NfL is a useful prognostic biomarker for PD, predicting clinical conversion to mild cognitive impairment or dementia. © 2021 International Parkinson and Movement Disorder Society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".