A proactive approach to migraine in primary care: a pragmatic randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Migraine is a common, disabling headache disorder that leads to lost quality of life and productivity. We investigated whether a proactive approach to patients with migraine, including an educational intervention for general practitioners, led to a decrease in headache and associated costs. METHODS: We conducted a pragmatic randomized controlled trial. Participants were randomized to one of two groups: practices receiving the intervention and control practices. Participants were prescribed two or more doses of triptan per month. General practitioners in the intervention group received training on treating migraine and invited participating patients for a consultation and evaluation of the therapy they were receiving. Physicians in the control group continued with usual care. Our primary outcome was patients' scores on the Headache Impact Test (HIT-6) at six months. We considered a reduction in score of 2.3 points to be clinically relevant. We used the Kessler Psychological Distress Scale (K10) questionnaire to determine if such distress was a possible effect modifier. We also examined the interventions' cost-effectiveness. RESULTS: We enrolled 490 patients in the trial (233 to the intervention group and 257 to the control group). Of the 233 patients in the intervention group, 192 (82.4%) attended the consultation to evaluate the treatment of their migraines. Of these patients, 43 (22.3%) started prophylaxis. The difference in change in score on the HIT-6 between the intervention and control groups was 0.81 (p = 0.07, calculated from modelling using generalized estimating equations). For patients with low levels of psychological distress (baseline score on the K10 ≤ 20) this change was -1.51 (p = 0.008), compared with a change of 0.16 (p = 0.494) for patients with greater psychological distress. For patients who were not using prophylaxis at baseline and had two or more migraines per month, the mean HIT-6 score improved by 1.37 points compared with controls (p = 0.04). We did not find the intervention to be cost-effective. INTERPRETATION: An educational intervention for general practitioners and a proactive approach to patients with migraine did not result in a clinically relevant improvement of symptoms. Psychological distress was an important confounder of success. (Current Controlled Trials registration no. ISRCTN72421511.).
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".