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2SPD-011 Comparative efficacy of eptinezumab, galcanezumab, fremanezumab and erenumab in the preventive treatment of chronic migraine

2023· article· en· W4367721148 on OpenAlexaboutno aff
R Claramunt García, CL Muñoz Cid, N García Gómez, T Sánchez Casanueva, M Merino Almazán

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChronic MigraineMedicinePlaceboMigraineInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

Background and Importance Several monoclonal antibodies for preventive treatment of chronic migraine have been approved in recent years. However, there are no studies that directly compare these treatments. Aim and Objectives To establish, through an indirect comparison (IC) against placebo, whether eptinezumab (Ep), galcanezumab (Ga), fremanezumab (Fre) and erenumab (Ere) could be considered equivalent alternatives in efficacy for the preventive treatment of chronic migraine. Material and Methods A PubMed search was performed for pivotal clinical trials (CTs) of eptinetumab (300 mg/12 weeks), galcanezumab (240 mg/4 weeks), fremanezumab (675 mg/12 weeks) and erenumab (140 mg/4 weeks) for the preventive treatment of chronic migraine. The variable for comparison was the percentage of patients with ≥75% response (% of patients with a 75% reduction in migraine days per month) at week 12 after the start of treatment. With the results of ≥75% response, relative risk (RR) compared to placebo was calculated. Finally, with these values, an IC of these drugs was performed using the Bucher method (ITC calculator, Indirect Treatment Comparisons, of the Canadian Agency for Health Technology Assessment). The results were analysed, seeing if there were statistically significant differences between these four drugs. Results Four CTs were found, one with each drug, all of them compared to placebo as a common comparator. All the studies presented a similar methodology. However, CT of erenumab was a phase 2 CT, while the others were phase 3. Moreover, in the erenumab CT the sample size (667 patients) was smaller than in the other CTs (between 1072 and 1130 patients). These limitations for IC were eventually accepted. After applying the Bucher method, the following results were obtained: OR (Ep 300 mg vs Gal2 40 mg) 0,89 [IC 95% 0,48–1,65]; p=0,70 OR (Ep 300 mg vs Fre 675 mg) 0,95 [IC 95% 0,56–1,61]; p=0,85 OR (Ep 300 mg vs Ere 140 mg) 1,21 [IC 95% 0,69–2,13]; p=0,50 OR (Fre 675 mg vs Gal 240 mg) 0,93 [IC 95% 0,46–1,89]; p=0,85 OR(Ere 140 mg vs Gal 240 mg ) 0,73 [IC 95% 0,35–1,52]; p=0,40 OR(Fre 675 mg vs Ere140 mg ) 1,28 [IC 95% 0,66–2,46]; p=0,47 Conclusion and Relevance According to the results obtained, given that no statistically significant differences have been established between the different drugs in terms of efficacy, the choice of one or the other should be based on safety and efficiency criteria. Nevertheless, it would be of special interest to have a direct comparison of these drugs to confirm the equivalence. References and/or Acknowledgements Conflict of Interest No conflict of interest

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.001

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.

Opus teacher head0.067
GPT teacher head0.361
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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