Autologous hematopoietic stem cell transplantation improves fatigue in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Fatigue is a common problem in multiple sclerosis (MS) affecting as many as 90% of patients. The Fatigue Impact Scale (FIS) is a validated measure of fatigue in MS patients. The cause of fatigue in MS is likely multifactorial, with some evidence that ongoing central nervous system (CNS) inflammation is a contributing factor. Immunoablation and autologous hematopoietic stem cell transplantation (aHSCT) have been shown to halt ongoing CNS inflammation. OBJECTIVE: To investigate whether halting all ongoing inflammation with aHSCT impacts FIS scores in patients with severe MS. METHODS: In the Canadian aHSCT study ( ClinicalTrials.gov , NCT01099930), 23 patients underwent aHSCT and had FIS prospectively collected every 6 months for 36 months of follow-up. Change in FIS was analysed by repeated-measures analysis of variance (RMANOVA) with multiple linear regression to determine independent predictors. RESULTS: = 0.001), and four patients had 100% reduction. Improvement in FIS correlated with lower age and Expanded Disability Status Scale at baseline, as well as increased independence as evidenced by a return to gainful employment and even driving. CONCLUSION: Patients had significantly less fatigue on average after aHSCT. This may serve to better understand the contribution of ongoing CNS inflammation to fatigue peculiar to MS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".