Alternate dosing of fingolimod in relapsing-remitting multiple sclerosis: A systematic review
Bibliographic record
Abstract
Background: Fingolimod is approved in relapsing-remitting multiple sclerosis (RRMS) with the recommended dose of 0.5 mg daily. To tackle possible adverse events, some clinicians may reduce the dose of fingolimod, mainly in the alternate-day form. We systematically reviewed the literature for efficacy measures of this method. Methods: PubMed (Medline®), Web of Science, Embase, Scopus, and the Cochrane Library databases were searched until April 9, 2021. Clinical studies (other than case reports and case series), in English, were included. Then, publications concerning alternate dose fingolimod (including every other day, every two or three days) were selected. Those studies concerning reduced daily dose (any daily dose less than 0.5 mg/day) were excluded to focus on alternate dosing. Results: Four observational studies were included. Data on Ohtani et al. study were limited. Three other studies were of good quality based on the Newcastle-Ottawa Scale. A total of 296 patients on the standard dose were compared to 276 patients on the alternate dosage. The most common reason for switching to the alternate dose was lymphopenia, followed by elevated liver enzymes. Two studies concluded that the alternate dosing could be a safe, yet effective strategy in patients with intolerable adverse effects of daily dose. However, Zecca et al. warned about the high possibility of disease reactivation. Due to the differences in outcome measures of the studies, meta-analysis was not applicable. Conclusion: This systematic review highlights the ambiguity of evidence on safety and efficacy of alternate dosing of fingolimod, encouraging further research on the subject.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".