The Prevalence and Long-Term Outcomes of Headaches after Aneurysmal Subarachnoid Hemorrhage: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Chronic headache after aneurysmal subarachnoid hemorrhage (aSAH) remains an ongoing area of investigation, with uncertainty regarding its prevalence and long-term outcomes. METHODS: A systematic review was conducted across five databases - Medline (Ovid), the Cochrane Central Register of Controlled Trials (Ovid), PsychInfo (Ovid), Embase (Ovid) and Scopus (Elsevier) - to identify all studies investigating the prevalence of chronic headaches after aSAH. A total of five studies were identified and included in our review. RESULTS: There were five observational studies (three cohort and two cross-sectional). The overall prevalence of chronic headaches after aSAH ranged from 16.1% to 41%, albeit across a follow-up time frame of 12 months to 7.5 years. Outcome measurements included quantitative pain scores and opioid usage; however, these were inconsistent across studies, and the studies did not address the long-term impacts of chronic headaches on quality of life or their psychosocial implications. CONCLUSION: The prevalence of chronic headache after aSAH is not well-characterized, and long-term outcomes are seldom studied, highlighting a critical gap in the current literature. Longitudinal cohort studies with standardized approaches to ascertain the psychosocial and physiological burden associated with post-aSAH chronic headaches are urgently needed.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| 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".