#36247 Glucagon-like peptide-1 analogue in the management of rebound intracranial hypertension: a case report
Bibliographic record
Abstract
Please confirm that an ethics committee approval has been applied for or granted: Not relevant (see information at the bottom of this page) Application for ESRA Abstract Prizes: I don’t wish to apply for the ESRA Prizes Background and Aims Rebound intracranial hypertension (RIH) is a complication in patients with spontaneous intracranial hypotension (SIH) following surgical repair of a cerebrospinal fluid (CSF) dura leak. Patients suffer from debilitating headache in supine position, that is usually temporarily, but could last for years. Typically, acetazolamide offers relief by decreasing CSF production, but patients can be(come) refractory. Recently, glucagon-like peptide-1 (GLP-1) analogues were proposed to modulating CSF secretion and reducing intracranial pressure. No studies have evaluated their use for RIH treatment. Methods A 46 year-old female patient with 1.5 years history of SIH developed RIH following surgical leak repair in 2017. She failed to maintain a good response of diuretics despite maximal dosage and failed other interventions. Pain score was high (NRS 6/10) and impacted quality of life, sleep and ability to work. In November 2021, she was initiated on Semaglutide 3 mg daily, and gradually increased over course of 3 months to 14 mg daily. Results The patient reported an immediate pain relief after starting Semaglutide, with further improvement as dose was increased. At 3 months, she reported significantly lower pain scores (NRS 1/10), improved sleep, resumption of part-time work and absence of side-effects. She remained on this drug on daily basis and was able to stop diuretics intake. Conclusions In this case, this GLP-1 agonist appeared to improve RIH symptom. Their role in the treatment of RIH should be evaluated in controlled studies to establish safety and efficacy. Consideration should be paid to how symptom improvement correlates (or not) with measurements of CSF pressure.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".