Multidisciplinary consensus guideline for the diagnosis and management of spontaneous intracranial hypotension
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it