OnabotulinumtoxinA Reduces Health Resource Utilization in Chronic Migraine: PREDICT Study
Bibliographic record
Abstract
BACKGROUND: PREDICT was a Canadian, multicenter, prospective, observational study in adults naïve to onabotulinumtoxinA treatment for chronic migraine (CM). We descriptively assess health resource utilization, work productivity, and acute medication use. METHODS: OnabotulinumtoxinA (155-195 U) was administered every 12 weeks over 2 years (≤7 treatment cycles). Participants completed a 4-item health resource utilization questionnaire and 6-item Work Productivity and Activity Impairment Questionnaire: Specific Health Problem V2.0. Acute medication use was recorded in daily headache diaries. Treatment-emergent adverse events were recorded throughout the study. RESULTS: A total of 197 participants were enrolled, and 184 received ≥1 treatment with onabotulinumtoxinA and were included in the analysis. Between baseline and the final visit, there were decreases in the percentage of participants who reported headache-related healthcare professional visit(s) (96.2% to 76.8%) and those who received headache-related diagnostic testing (37.5% to 9.9%). Reductions from baseline were also observed in the mean number of headache-related visits to an emergency room/urgent care clinic (2.5 to 1.4) and median headache-related hospital admissions (4.0 to 1.0). OnabotulinumtoxinA improved work productivity and reduced the mean (standard deviation) number of hours missed from work over a 7-day period (6.1 [9.7] to 3.0 [6.8]). Mean (standard deviation) acute medication use decreased from baseline (15.2 [7.6] to 9.1 [6.5] days). No new safety signals were identified. CONCLUSIONS: Real-world evidence from PREDICT demonstrates that onabotulinumtoxinA treatment for CM in the Canadian population reduces health resource utilization and acute medication use and improves workplace productivity, supporting the long-term benefits of using 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".