Trajectory of health care resources among adults stopping or reducing treatment frequency of botulinum toxin for chronic migraine treatment in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: Understand health resource, medication use, and cost of adults with chronic migraine who received guideline-recommended onabotulinumtoxinA (botulinum toxin) treatment frequency and then continued or reduced/stopped. BACKGROUND: Botulinum toxin may be a beneficial treatment for chronic migraine; the trajectory of health resources utilization among those with continued or reduced/stopped use is unclear. METHODS: A retrospective population-based cohort study utilizing administrative data from Alberta, Canada (2012-2020), was performed. A cohort of adults who received ≥5 botulinum toxin treatment cycles for chronic migraine over 18 months (6-month run-in; 1-year pre-index period) were grouped into those who (1) continued use (≥3 treatments/year), or (2) stopped or reduced use (stopped for 6 months then received 0 or 1-2 treatments/year, respectively) over a 1-year post-index period. Health resources and medication use were described, and pre-post costs were assessed. A second cohort that received ≥3 treatments/year immediately followed by 1 year of stopped or reduced use was considered in sensitivity analysis. RESULTS: Pre-post health resource, medication use, and costs were similar among those with continued use (n = 3336). Among those who stopped or reduced use (n = 1099; 756 stopped, 343 reduced), health resource, medication use, and costs were lower in the post- (total median per-person cost [IQR]: all-cause $4851 [$8090]; migraine-related $835 [$1915]) versus pre- (all-cause $6096 [$7207]; migraine-related $2995 [$1950]) index period (estimated cost ratios [95% CI]: total all-cause 0.86 [0.79, 0.95]; total migraine-related 0.44 [0.40, 0.48]). In the second cohort (n = 3763), return to continued use (≥3 treatments/year) occurred in up to 70.4% in those with reduced use. CONCLUSIONS: Of adults treated with botulinum toxin for chronic migraine, 75.2% had continued use, stable health resource and medication use, and costs over a 2 year period. In those that stopped/reduced use, the observed lower health resource and migraine medication use may indicate improved symptom control, but the resumption of guideline-recommended treatment intervals after reduced use was common.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".