An overview of reviews with network meta-analyses comparing disease-modifying therapies for relapsing multiple sclerosis
Bibliographic record
Abstract
Aim: An overview of published systematic reviews (SRs) with integrated network meta-analyses (NMAs) comparing disease-modifying therapies (DMTs) for relapsing multiple sclerosis (RMS) was conducted to help inform healthcare decision-making. Methods: We searched Embase, MEDLINE® and Cochrane Database of Systematic Reviews from inception to May 2023. Full-text studies evaluating annualized relapse rate (ARR) and/or confirmed disability progression (CDP) were qualitatively compared. Methodological quality was assessed using SR and NMA questionnaires. Results: Twenty-one SRs with integrated NMAs were included. Studies varied in their conduct and reporting of the SR, the included primary evidence, treatments, and cross-trial heterogeneity assessment and their conduct and reporting of NMAs. The quality of the studies was variable. Monoclonal antibody therapies were determined to be the most efficacious DMTs for reducing ARR and delaying CDP. Conclusion: Future analyses should carefully consider and clearly report methods and results to permit accurate interpretation of NMA findings and better inform decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
| 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".