Rimegepant, Ubrogepant, and Lasmiditan in the Acute Treatment of Migraine Examining the Benefit-Risk Profile Using Number Needed to Treat/Harm
Bibliographic record
Abstract
OBJECTIVES: To develop and compare benefit-risk profiles for rimegepant, ubrogepant, and lasmiditan based on a network meta-analysis (NMA) of published clinical trials. METHODS: A fixed-effects Bayesian NMA of randomized controlled trials of lasmiditan, rimegepant, and ubrogepant for the acute treatment of adults with migraine were used to determine risk differences for efficacy and safety outcomes of the 3 treatments compared with pooled placebo. Risk differences were used to calculate number needed to treat (NNT) for pain relief and pain freedom at 2 and 2 to 24 hours and freedom from most bothersome symptoms at 2 hours; and number needed to harm (NNH) for dizziness and nausea, relative to placebo. RESULTS: Results were based on 5 randomized controlled trials (NCT03461757, NCT02828020, NCT02867709, NCT02439320, and NCT02605174). NNT to achieve sustained pain relief at 2 to 24 hours was lowest for rimegepant 75 mg (5; 95% credible interval [Crl]: 4, 7) and ubrogepant 100 mg (5; 95% Crl: 4, 8) and highest for ubrogepant 25 mg (8; 95% Crl: 5, 16). Rimegepant had the lowest NNT to achieve sustained pain freedom at 2 to 24 hours and lasmiditan 50 mg had the highest (7; 95% Crl: 5, 12 vs. 26; 95% Crl: 13, 95). NNH for dizziness and nausea was highest for ubrogepant 25 mg (28; 95% Crl: 15, 62 and 99; 95% Crl: -2580, 2378, respectively). Lasmiditan 200 mg had the lowest NNH for dizziness and rimegepant 75 mg had the lowest NNH for nausea. CONCLUSIONS: The benefit-risk profiles of lasmiditan, rimegepant, and ubrogepant may improve clinical decision-making.
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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 | 0.084 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.053 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".