2606 PREDICT: PREEMPT fixed-dose, fixed-site and follow the pain
Bibliographic record
Abstract
Objective Analyse the real-world effectiveness and safety of 155U, 156–195U and 195U onabotulinumtoxinA in patients with chronic migraine (CM) from the PREDICT study. Methods PREDICT (NCT02502123) was a Canadian 2-year, prospective, observational study in adults with CM. Patients received onabotulinumtoxinA approximately every 12 weeks (≤7 treatment cycles [Tx]). The primary endpoint was mean change from baseline in Migraine-Specific Quality of Life (MSQ) at Tx4. Headache days and physician and patient satisfaction were evaluated throughout. This analysis stratified the safety population (≥1 dose) into 3 groups (155U,156–195U and 195U) by the dose received on ≥3 of the first 4 treatment cycles. Results 184 patients received ≥1 onabotulinumtoxinA dose, 68 received 155U, 65 received 156–195U and 13 received 195U on ≥3 treatments. Baseline characteristics were similar between groups. Baseline mean (SD) headache days/month 21.6(6.4) 155U; 20(7) 156–195U; and 21.7(6) 195U decreased over time (Tx4: -7.1[6.7] 155U; -6.5[6.7] 156–195U; -11.2[6.4] 195U versus baseline). All MSQ domains improved in all groups at Tx4 and at the final visit. Physicians rated most patients as improved, and most patients were satisfied at final visit (80.8% 155U; 83.6% 156–195U; 90% 195U). Treatment-emergent adverse events (TEAEs) were reported in 18/68 patients (26.5%) in 155U, 41/65(63.1%) in 156–195U and 10/13(76.9%) in 195U; treatment-related TEAEs were 9(13.2%), 10(15.4%) and 3(23.1%) respectively; serious TEAEs were 0, 3(4.6%) and 1(7.7%), none were considered treatment-related. Conclusion Consistent with PREEMPT trials and REPOSE observational study, long-term treatment with onabotulinumtoxinA in PREDICT was safe, well-tolerated, and effective in CM. No new safety signals were identified.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".