Which came first, the risk of migraine or the risk of asthma? A systematic review
Bibliographic record
Abstract
OBJECTIVES: We conducted this review to systematically assess the association and risk of the migraine in the patient with asthma and vice versa. METHODS: We systematically searched publishes articles indexed in PubMed, Scopus, Cochrane library, PsycINFO, CINAHL, ISI Web of Science, Science Direct from inception, and Embase databases until June 2017. The quality assessment of the involved studies was done using the Newcastle-Ottawa Scale (NOS). RESULTS: Eight studies with 389,573 participants were reviewed and selected for data extraction. Among the selected studies, 5 were reported the association between migraine with asthma risk, and the rest three studies reported the risk of asthma in patient with migraine compared to non-moraine individuals. Odds ratio (OR) of migraine for patient with asthma as compared with non-asthmatic individuals was 1.62 (95% CI 1.43-1.82). Data pooling using a random-effect model showed that migraine was associated with a significant increased risk of asthma (relative risk (RR): 1.56; 95% CI: 1.51-1.60; p < .00001). Besides, sub-group and sensitivity analyses supported the positive association between asthma and migraine, and risk of asthma in migraine patients. CONCLUSION: Now it is unknown if control of the asthma will impact the severity of migraines or vice versa, but it is necessary to perform more research to further explain the mechanisms through which asthma increases the frequency of migraine or vice versa. If two conditions linked, once an individual undergo better control of asthma symptoms, might the excruciating migraine ease, too.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".