Possibilities of magnetic resonance morphometry and laboratory biomarkers in studying the progression of multiple sclerosis
Bibliographic record
Abstract
Objective. To show that magnetic resonance morphometry and laboratory biomarkers are promising methods for early detection of progressive forms of multiple sclerosis (MS). Material and methods. Eighty-one patients with MS were examined, magnetic resonance morphometry was performed in all of them, 60 patients were analyzed for neurofilament light chains (sNFL), phosphorylated neurofilament heavy chains (spNFH) and glial fibrillary protein (sGFAP) in serum by enzyme-linked immunosorbent assay. Results. Brain volumes were negatively correlated with disease duration, EDSS score, 25-foot walk test score and 9-ring test and positively correlated with the Symbol-Numeric Test and the Montreal Cognitive Assessment. Patients with progressive types of MS (PMS) had smaller volumes of brain gray matter, cerebellar white matter, occipital lobes, caudate nucleus, hippocampus, pallidum, thalamus, and contiguous nucleus. A CSF volume greater than 15.06% could suggest progression (CI 54.79—91%) with a sensitivity of 77.78% and specificity of 70.18%. When patients were on DMT, they had larger thalamic volumes (median 1.09% [1.6; 1.16] vs 1.04% [0.95; 1.14]; p=0.02) and smaller CSF volumes (13.86±2.87% vs. 15.55±3.49%; p=0.03). The levels of sNFL and spNFH were not increased in PMS and during exacerbations, and the low obtained values of sNFL suggest poor sensitivity of the method. There were trends (p=0.374) towards higher sGFAP in patients with PRS (median 3.2 ng/mL [1.85; 4.6] compared to remitting MS (2.05 ng/mL [1.29; 4.52]). Conclusion. The results demonstrate the differences in brain volumes in patients with different types of MS and emphasize the importance of long-term follow-up to better assess disease 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".