Predictors of episodic migraine transformation to chronic migraine: A systematic review and meta-analysis of observational cohort studies
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: An estimated 2.5-3.1% of people with episodic migraine develop chronic migraine in a year. Several risk factors are associated with an increased risk for this transformation. We conducted a systematic review and meta-analysis to provide quantitative and qualitative data on predictors of this transformation. METHODS: An electronic search was conducted for published, prospective, cohort studies that reported risk factors for chronic migraine among people with episodic migraine. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale. Quality of evidence was determined according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines. Effect estimates were retrieved and summarized using risk ratios. RESULTS: Of 5695 identified publications, 11 were eligible for inclusion. The pooled analysis (GRADE system) found "high" evidence for monthly headache day frequency ≥ 10 (risk ratio = 5.95), "moderate" evidence for depression (risk ratio = 1.58), monthly headache day frequency ≥ 5 (risk ratio = 3.18), and annual household income ≥ $50,000 (risk ratio = 0.65) and "very low" evidence for allodynia (risk ratio = 1.40) and medication overuse (risk ratio = 8.82) in predicting progression to chronic migraine. CONCLUSIONS: High frequency episodic migraine and depression have high quality evidence as predictors of the transformation from episodic migraine to chronic migraine, while annual household income over $50,000 may be protective.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".