OnabotulinumtoxinA Improves Quality of Life in Chronic Migraine: The PREDICT Study
Bibliographic record
Abstract
ABSTRACT Background: The PREDICT study assessed real-world, long-term health-related quality of life in adults with chronic migraine (CM) receiving onabotulinumtoxinA. Methods: Canadian, multicenter, prospective, observational study in adults naïve to onabotulinumtoxinA for CM. OnabotulinumtoxinA (155–195 U) was administered every 12 weeks over 2 years (≤7 treatment cycles). Primary endpoint: mean change in Migraine-Specific Quality of Life Questionnaire (MSQ) at treatment 4 (Tx4) versus baseline. Secondary endpoints: mean change in MSQ at final visit versus baseline, and headache days. Results: 184 participants (average age 45 years; 84.8% female; 94.6% Caucasian) received ≥1 onabotulinumtoxinA treatment; 150 participants completed 4 treatments (1 year) and 123 completed all 7 treatment cycles (2 years). Mean (SD) onabotulinumtoxinA dose per treatment cycle was 171 (18) U and treatment interval was 13.2 (1.8) weeks. Baseline mean (SD) 20.9 (6.7) headache days/month decreased (Tx1: −3.5 [6.3]; Tx4: −6.5 [6.6]; p < 0.0001 versus baseline). Mean (SD) increased from baseline in MSQ at Tx4 (restrictive: 21.5 [24.3], preventive: 19.5 [24.7], emotional: 22.9 [32.9]) and the final visit (restrictive: 21.3 [23.0], preventive: 19.2 [23.7], emotional: 27.4 [30.7]), exceeding minimal important differences (all p < 0.0001). Seventy-seven (41.8%) participants reported 168 treatment-emergent adverse events (TEAEs); 38 TEAEs (12.0%) were considered treatment-related. Four (2.2%) participants reported six serious TEAEs; none were considered treatment-related. No new safety signals were identified. Conclusions: Real-world evidence from PREDICT demonstrates that onabotulinumtoxinA for CM in Canada improved MSQ scores and reduced headache frequency and severity, adding to the body of evidence on the long-term safety and effectiveness of onabotulinumtoxinA for CM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".