Prevalence and risk factors of ischemic stroke-related headache in China: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Headache accompanying ischemic stroke is considered an independent predictor of neurological deterioration. This meta-analysis aims to estimate the prevalence of ischemic stroke-related headaches and identify its risk factors in China. METHODS: PubMed, Embase, Cochrane Library database, Web of Science, PsycINFO, and four Chinese databases for the related publications were searched. Two researchers independently selected the literature, extracted the relevant data, and assessed its methodological quality. The meta-analysis applied a random-effects model with R software to calculate the pooled prevalence of ischemic stroke-related headaches in Chinese patients, and to merge the odds ratio (OR) of risk factors. Subgroup analysis, sensitivity analysis, and meta-regression analysis were conducted. Publication bias was assessed by a funnel plot and Egger test. RESULTS: Ninety-eight studies were eligible for inclusion. The overall pooled prevalence of ischemic stroke-related headache was 18.9%. Subgroup analysis showed that the prevalence of ischemic stroke related-headaches was higher among studies using self-report to diagnosis headache (18.9%; 95%CI, 8.9% to 40.2%), and those focused on age ≥ 55 years (19.7%; 95%CI, 14.9% to 25.9%), rural settings (24.9%; 95%CI, 19.7% to 31.6%). There were no significant differences in the headache prevalence between studies in the south and north, and inland and coastal studies. The prevalence of pre onset headache (13.9%) and tension-type headache (15.5%) and was higher compared with other types. History of headache (OR = 3.24; 95%CI, 2.26 to 4.65.), female gender (OR = 2.06; 95%CI, 1.44 to 2.96.), midbrain lesions (OR = 3.56; 95%CI, 1.86 to 6.83.), and posterior circulation stroke (OR = 2.13; 95%CI, 1.14 to 4.32) were major risk factors. CONCLUSION: The prevalence of ischemic stroke-associated headache is high in China. In addition, women, presence of midbrain lesions, posterior circulation stroke and a history of migraine were high-risk factors for ischemic stroke-related headaches. Designing effective interventions to prevent or alleviated headaches is necessary to promote patients' neurological recovery and quality of life.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".