Multidisciplinary consensus guideline for the diagnosis and management of spontaneous intracranial hypotension
Bibliographic record
Abstract
BACKGROUND: We aimed to create a multidisciplinary consensus clinical guideline for best practice in the diagnosis, investigation and management of spontaneous intracranial hypotension (SIH) due to cerebrospinal fluid leak based on current evidence and consensus from a multidisciplinary specialist interest group (SIG). METHODS: A 29-member SIG was established, with members from neurology, neuroradiology, anaesthetics, neurosurgery and patient representatives. The scope and purpose of the guideline were agreed by the SIG by consensus. The SIG then developed guideline statements for a series of question topics using a modified Delphi process. This process was supported by a systematic literature review, surveys of patients and healthcare professionals and review by several international experts on SIH. RESULTS: SIH and its differential diagnoses should be considered in any patient presenting with orthostatic headache. First-line imaging should be MRI of the brain with contrast and the whole spine. First-line treatment is non-targeted epidural blood patch (EBP), which should be performed as early as possible. We provide criteria for performing myelography depending on the spine MRI result and response to EBP, and we outline principles of treatments. Recommendations for conservative management, symptomatic treatment of headache and management of complications of SIH are also provided. CONCLUSIONS: This multidisciplinary consensus clinical guideline has the potential to increase awareness of SIH among healthcare professionals, produce greater consistency in care, improve diagnostic accuracy, promote effective investigations and treatments and reduce disability attributable 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 | 0.032 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".