Serum glial fibrillary acidic protein as a marker of brain MRI metrics in multiple sclerosis: A scoping review
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Magnetic resonance imaging (MRI) is heavily relied upon for the diagnosis and monitoring of multiple sclerosis (MS), a chronic, demyelinating disease of the central nervous system. Serum biomarkers may serve as an accessible tool for increasing sensitivity, improving accessibility, corroborating symptoms, and providing additional data to guide clinical management. This scoping review investigates the current understanding of how the serum biomarker glial fibrillary acidic protein (sGFAP) relates to brain MRI metrics. METHODS: We adhered to the Joanna Briggs Institute methodology for scoping reviews and the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. The databases Medline (Ovid), Embase (Ovid), CINAHL (Ebsco), and Web of Science (University of British Columbia institutional access) were searched on August 24, 2023 using a combination of medical subject headings and keyword terms for the topic of serum biomarkers in MS. RESULTS: A total of 9880 articles were retrieved in total of which 6271 unique titles and abstracts were screened. Twelve of the 259 resultant papers contained sGFAP data and proceeded to extraction. It was found that lesion MRI metrics generally had a positive relationship with sGFAP, while gray matter and white matter metrics, including normal-appearing white matter, were related negatively or not at all. CONCLUSIONS: These results highlight that while sGFAP may not be specific for MS, it may have utility for increasing sensitivity in postdiagnosis monitoring of MS progression.
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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 | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".