The Impact of Revascularization Surgery on Headaches in Association with Cerebrovascular Reactivity in Patients with Moyamoya Angiopathy
Bibliographic record
Abstract
Background/Objectives: Headaches in Moyamoya angiopathy are common but poorly understood. We aimed to investigate if headaches in Moyamoya angiopathy improve after revascularization surgery and whether this is associated with improvement in cerebrovascular reactivity on MRI (CVR-MRI). Methods: We included consecutive adult patients with Moyamoya angiopathy who had chart data on headaches, CVR-MRI, and underwent extracranial–intracranial bypass surgery between January 2010 and September 2022 at a tertiary neurovascular referral center. Clinical and CVR-MR imaging data of all patients were collected through systematic chart review, complemented by standard-of-care headache questionnaires from patients who were operated between 2018 and 2022. We evaluated headache features and explored the association between headaches and CVR before and after revascularization surgery. Results: Fifty-nine patients were included (mean age 47 ± 14 years, 43 females (73%)); among them, 41/59 (69%) reported headaches pre-surgery. Headache improved in 28/41 (68%) patients after revascularization surgery with a reduction in pain severity (median VAS-score from 5/10 to 2.5/10; p = 0.002), analgesic use (from 84% to 40%; p = 0.007), and sick leave (from 60% to 16%; p < 0.001). Improvement in headaches was associated with improvement in CVR (OR 5.3; 95% CI: 1.2–23.5) and sick leave reduction (OR 1.4; 95% CI: 1.6–121.4). Conclusions: Headaches in Moyamoya angiopathy are common and disabling. They may improve in most patients after revascularization surgery and seem to be associated with improvement in CVR, supporting the hypothesis of a potential vascular origin of the headaches.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".