P.114 Headache outcomes after treatment of unruptured intracranial aneurysms: systematic review and meta-analysis
Bibliographic record
Abstract
Background: Headaches are a major cause of disability and healthcare cost worldwide. When investigating headaches etiology, incidental unruptured intracranial aneurysms are often considered unrelated. We conducted a systematic review and meta-analysis to assess headaches outcomes (severity) after treatment of unruptured intracranial aneurysm. Methods: MEDLINE and EMBASE were systematically reviewed. Results: The data from eligible studies (n=7) was extracted and analyzed. 309 nonduplicated patients provided patient-level data for analysis. All studies used the 10-point numeric rating scale (NRS). 88% of patients were treated with endovascular technique. Overall, the observed effect estimate under a random effects model was found to be a standard mean difference in pre- and post-intervention headache severity of -0.448 (95% CI: -0.566 to -0.329). No significant heterogeneity was noted. No significant publication bias was demonstrated. Conclusions: This is the first and largest systematic review assessing postoperative headache outcomes after treatment of unruptured intracranial aneurysm. A significant reduction in headache intensity after treatment is observed in the current published literature. This study highlights an interesting clinical phenomenon that still warrants scientific effort before it can influence clinical practice. We encourage future study to stratify headache outcomes by aneurysm size, location and treatment modality.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.033 |
| Bibliometrics | 0.006 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".