Botulinum Toxin Type A and Acute Drug Costs in Migraine with Triptan Overuse
Bibliographic record
Abstract
BACKGROUND: Patients with chronic migraine and medication overuse are significant consumers of health care resources. OBJECTIVE: To determine whether botulinum toxin type A prophylaxis reduces the cost of acute migraine medications in patients with chronic migraine and triptan overuse. METHODS: In this multicenter, open-label study, patients with chronic migraine (≥ 15 headache days/month) who were triptan overusers (triptan intake ≥ 10 days/month for ≥ 3 months) received botulinum toxin type A (95-130 U) at baseline and month three. Headache (HA) frequency and medication use were assessed with patient diaries, and headache-related disability by means of the MIDAS and Headache Impact Test-6 questionnaires. RESULTS: Of 53 patients enrolled (mean age ± standard deviation, 46.5 years ± 8.4; 47 [88.7%] females), 48 (90.6%) completed the study at month six. Based on headache diaries, significant (P ≤ 0.0002) decreases from baseline were observed for days per month with headache/migraine, days with any acute headache medication use, days with triptan use, and triptan doses taken per month. A significant (P < 0.0001) increase from baseline in headache-free days per month was also observed. Prescription medication costs for acute headache medications decreased significantly, including significant reductions in triptan costs (mean reduction of -C$106.32 ± 122.87/month during botulinum toxin type A prophylaxis; P < 0.0001). At baseline, 78% of patients had severe disability (MIDAS score) and 86.8% had severe impact due to headache (HIT-6 scores); at month six, this decreased to 60% and 68%, respectively. CONCLUSIONS: Botulinum toxin type A prophylactic therapy markedly decreased costs related to acute headache medication use in patients with chronic migraine and triptan overuse.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".