Fluid biomarkers of white matter hyperintensities in cerebrovascular disease and neurodegeneration: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The goal of this systematic review is to evaluate the association between fluid biomarkers and white matter hyperintensities (WMH) in cerebrovascular disease and neurodegenerative disorders. While previous research has examined the etiology of WMH in specific diseases, we propose a comprehensive framework encompassing WMH of both vascular and non-vascular origin. INTRODUCTION: Although WMH have been mostly described in aging populations with cerebrovascular disease, extensive lesions also occur in non-vascular diseases. Such lesions are traditionally treated as a separate pathological entity from vascular ones, but recent work has challenged the appropriateness of that framework when probing WMH etiology. Comparing biomarkers associated with WMH across various pathologies may improve our understanding of their etiology. INCLUSION CRITERIA: The review will focus on cerebrovascular disease and neurodegenerative disorders and exclude infectious, metabolic, drug-induced, or radiation-induced white matter diseases. Original, peer-reviewed research on the relationship of WMH on magnetic resonance imaging with blood/cerebrospinal fluid biomarkers will be considered for inclusion. Postmortem studies will guide the selection of biomarkers of interest and the interpretation of our findings. Genomic markers will be excluded. METHODS: The review will be conducted in accordance with PRISMA and JBI guidelines. English articles of interest published between 2000 and 2020 will be identified in MEDLINE and Embase. Two reviewers will perform abstract and full-text screening, standardized data extraction, and quality assessments of the selected studies. The relationship between each biomarker and WMH burden will be meta-analyzed, if possible, with subgroup or meta-regression analyses to assess differences between diseases. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42020218298.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".