Plasma NFL, GFAP, sTREM2, pTau231, and clinical severity in progressive supranuclear palsy
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate four plasma markers, pTau231, sTREM2, neurofilament light chain (NFL), and glial fibrillary acidic protein (GFAP), in relation to disease severity in a Korean cohort of progressive supranuclear palsy (PSP). METHODS: Baseline data from patients with probable PSP enrolled in the SCANPSP cohort (NCT05579301) were compared with those of age-matched healthy controls (HC). Four plasma markers measured using the Simoa method, clinical findings, and volumetric 3 T MRI were investigated. RESULTS: ) and global cognitive scores (r = -0.34, p = 0.0061 for Mini-Mental Status Exam: r = -0.32, p = 0.0088 for Montreal Cognitive Assessment). Intercorrelations between NFL, pTau231, and GFAP were observed in patients with PSP but no intercorrelations in HC. The combination of pTau231, NFL, and GFAP yielded the highest ability to distinguish the PSP-Richarson syndrome (PSP-RS) from the PSP-subcortical, with an area under the curve of 0.80. The gray matter voxel-wise correlation of whole patient data showed that NFL correlated with third ventricle enlargement and frontal/cingulate atrophy, whereas sTREM2 correlated with third ventricle and thalamic volumes. White matter analyses revealed that the supplementary motor area and vermis correlated with NFL in whole PSP. In PSP-RS group, the superior frontal gyrus was associated with NFL, and the cerebellar crus with sTREM2. CONCLUSION: This study demonstrated the feasibility of using four plasma markers simultaneously to determine PSP disease severity. To verify these findings, longitudinal analyses are necessary.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".