Quality of life in patients with spontaneous intracranial hypotension: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Not only are diagnosis and management of spontaneous intracranial hypotension (SIH) challenging due to heterogeneous symptoms and limited treatment effectiveness, but SIH's impact on health-related quality of life (HRQoL) is under-documented. OBJECTIVES: In this systematic review, we aim to evaluate the assessment of QoL in SIH patients, identify impacted QoL domains, and explore treatment-related changes in QoL with a meta-analysis. METHODS: Following PRISMA recommendations, we conducted a systematic literature search using a comprehensive set of keywords related to QoL and SIH. Databases were searched from the inception to July 2025. Studies were included if they provided reports on the quality of life for SIH patients. A meta-analysis using mean difference (MD) of baseline and after-treatment QoL scores was conducted. The risk of bias was assessed using the Newcastle-Ottawa scale. RESULTS: Of 1435 initial publications, 20 studies met the inclusion criteria, representing a total of 1106 patients with SIH. EQ-5D-5L and HIT-6 were the most frequently used tools, with pooled results showing significant improvement post-treatment in perceived health (Visual analog scale score improved from 38.9 to 72.2; MD of 42.4 [95% CI 26.2-58.7]) and headache impact (HIT-6 scores improved from 66.1 to 49.3; MD of 20.1 [95% CI: 14.7-25.6]). Despite treatment, studies reported moderate to severe physical, mental, and social limitations. DISCUSSION: The reporting of QoL is inconsistent and the tools used to assess QoL in SIH patients are heterogenous. While treatment provides help, some symptoms persist and highlight the need for specific QoL assessment, with tools tailored to SIH.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".