P.011 OnabotulinumtoxinA, quality of life, health resource utilization, and work productivity in chronic migraine: interim results from PREDICT
Bibliographic record
Abstract
Background: We assessed long-term health-related quality of life (HRQoL) and functioning in adults receiving onabotulinumtoxinA for CM. Methods: Interim analysis of multicentre, prospective, observational study in adults naïve to botulinum toxin (NCT02502123). Mean change from baseline in Migraine-Specific Quality of Life (MSQ) score (primary); healthcare resource utilization (HRU) and work productivity (secondary) assessed in patients receiving 4 of 7 onabotulinumtoxinA treatments (Tx4; ~10 months). Results: Across treatments (baseline, n=196, post-Tx2, n=173, post-Tx4, n=137), the mean (SD) between-session interval and onabotulinumtoxinA dose was 13.1 weeks and 170.4 (17.2) U, respectively. MSQ scores increased significantly (P<0.0001) (baseline to post-Tx4; all role function domains). Patient percentages declined from baseline to post-Tx2 and post-Tx4 for emergency room visits (17.3%; 9.3%; 6.6%), hospital admissions (3.6%; 2.9%; 1.5%), and headache-related diagnostic testing (35.9%; 15.9%; 8.1%). The percentages of patients employed at baseline (73.5%) and post-Tx4 (72.3%) were similar. Hours worked increased slightly from baseline to post-Tx4 (28.0 [SD=15.4]; 29.4 [SD=16.0]). Headache-related missed work hours decreased (5.9 [SD=9.5]; 2.5 [SD=5.9]). Patients reported less headache-related impact on work productivity from baseline to post-Tx4 (5.4 [SD=2.1] vs 3.9 [SD=2.6]) and ability to perform daily activities (6.1 [SD=2.1] vs 4.2 [SD=2.8]). Conclusions: OnabotulinumtoxinA for CM improved HRQoL and work productivity and reduced HRU.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".