The association of serum carnitine levels with severity of fatigue in patients with multiple sclerosis: A pilot study
Bibliographic record
Abstract
Background: Fatigue is a common complaint of patients with multiple sclerosis (MS), adversely affecting their quality of life. There is a lot of evidence showing that carnitine deficiency is linked to fatigue development and severity in some conditions. This study aimed to evaluate the association between free L-carnitine serum levels and the severity of fatigue in patients with MS. Methods: This case-control study included 30 patients with relapsing-remitting MS (RRMS) in two age-matched equal-number groups according to the presence or absence of fatigue. Fatigue was scored using the valid questionnaire of Fatigue Severity Scale (FSS) and serum level of free L-carnitine was measured simultaneously. Finally, the association between serum level of free L-carnitine and fatigue severity was evaluated in patients with MS. Results: The mean value of FSS in patients with fatigue was 48.80 ± 8.55, which was nearly two-fold higher than the group without fatigue. We found a significant correlation between the serum level of free L-carnitine and FSS and showed that the patients with fatigue had a significantly lower serum level of free L-carnitine compared to patients without fatigue (P < 0.001). Conclusion: Present study demonstrated that patients with lower serum levels of free L-carnitine were more likely to experience fatigue. We recommend that a higher dietary intake of carnitine might be a useful complementary treatment for MS-related fatigue.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".