The association of serum carnitine levels with severity of fatigue in patients with multiple sclerosis: A pilot study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it