Evaluation of the effectiveness of intensive medical rehabilitation in MS patients through the prism of functional tests and serum neurofilaments
Bibliographic record
Abstract
Objective. To evaluate the effect of intensive medical rehabilitation on serum neurofilament (NF) levels and performance in multiple sclerosis (MS) patients. Material and methods. The study included 39 patients with various forms of MS (remitting, secondary progressive, and primary progressive). Participants underwent a comprehensive assessment of motor and cognitive functions and quality of life (EDSS and Berg scales, 5STS, 6MWT, MOCA, SDMT, MSQOL-54 tests). The serum levels of NFL (NF light chains) and NFH (NF heavy chains) were measured using ELISA. The medical rehabilitation program included a 10-day inpatient course and an 8-day telerehabilitation. Follow-up measurements were performed after medical rehabilitation was completed. Results. There was no statistically significant decrease in the level of serum neurofilaments after the medical rehabilitation (p=0.686). However, there was a significant improvement in cognitive function (MoCA and SDMT scales, Beck’s inventory, p<0.01), motor performance (Berg scale, EDSS, 5STS, 6MWT, p<0.05), and quality of life. A correlation between NF level and disability indicators (EDSS) and cognitive status was found. Conclusion. The study’s results confirm that intensive medical rehabilitation improves motor and cognitive functions in patients with MS but does not significantly affect the NF level, which indicates predominantly functional rather than structural changes in the nervous system. It emphasized the importance of a combined approach to MS management, including drug therapy and medical rehabilitative interventions. Studies with larger samples and more sensitive NF analysis methods, such as Simoa, are needed to further investigate the effect of medical rehabilitation on neurodegenerative processes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".