Risk of Stroke in Migrainous Women, a Hidden Association: A Systematic Review
Bibliographic record
Abstract
Migraine-a term used to describe a unilateral throbbing headache has shown growing evidence of being linked to different types of strokes-particularly ischemic and hemorrhagic. This study aims to identify and summarize the relationship between migraine and the incidents of stroke in women of child-bearing age. This systematic review was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search was done using PubMed, the British Medical Journal (BMJ), Cochrane library, Google Scholar, and ScienceDirect databases up until March 15, 2022. Studies were chosen based on the listed eligibility criteria: English-language, observational studies, systematic reviews, articles, and meta-analyses, which included stroke patients and migraine patients, and the possible link between these two conditions. In addition, quality assessment was done using assessment tools like Scale for the Assessment of Narrative Review Articles (SANRA), Assessment of multiple systematic reviews (AMSTAR), and Newcastle-Ottawa Scale (NOS) criteria. The initial search generated 245 studies. Fourteen studies were included in the final selection - one case-control, four cohort studies, seven systematic reviews with meta-analyses, and two narrative reviews. Strokes-particularly ischemic-were found to be linked to the incidents of migraine in women. The risks of a stroke increased if a woman was a smoker, under 45, and uses oral contraceptives regularly. In addition, the use of nonsteroidal anti-inflammatory drugs (NSAIDs), genetic predisposition, and metabolic dysfunction was linked to increased incidents of hemorrhagic strokes-which proved to be rarer but more fatal due to their serious underlying pathophysiologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".