Cerebrospinal fluid and blood levels of neurofilament light chain in Parkinson disease
Bibliographic record
Abstract
BACKGROUND: Parkinson disease (PD) is a common neurodegenerative disorder. Elevations of neurofilament light chain (NfL) concentrations in the cerebrospinal fluid (CSF) and blood are a marker of neuronal/axonal injury and degeneration. However, CSF and blood NfL alterations in patients with PD from existing studies remain inconclusive. To better understand these conflicting data, we will conduct a meta-analysis. METHODS: We will comprehensively search PubMed, Embase, and Web of Science databases from each database's inception to 7th June, 2020. This protocol will conform to the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. We will only include original studies published in English that evaluated differences of NfL concentrations in the CSF or blood between idiopathic PD patients and healthy controls. The Newcastle-Ottawa Scale will be used to evaluate the quality of the included studies. Meta-analyses will be carried out using the STATA software version 13.0. Between-group difference of NfL concentrations in the CSF and blood will be expressed as the weighted standardized mean difference. A random-effects model will be used. Supplementary analyses, such as heterogeneity analysis, sensitivity analysis, publication bias, subgroup analysis, and meta-regression analysis will be performed. RESULTS: The meta-analysis will provide the differences of NfL concentrations in the CSF and blood between patients with PD and healthy controls and will show the magnitudes of their effect sizes. CONCLUSIONS: This meta-analysis will provide the evidence of NfL concentrations in the CSF and blood in PD and we hope that our study has an important impact on clinical practice. REGISTRATION NUMBER: INPLASY202060025.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".