Multiple spontaneous cerebrospinal fluid leaks: a rare case report and review of literature
Bibliographic record
Abstract
While a spontaneous cerebrospinal fluid leak (sCSFL) is a rare phenomenon, patients who present with multiple cerebrospinal fluid leaks (mCSFLs) are an even rarer entity. There have been documented cases reported over the last decades, but many with only limited descriptions of each patient. We present a mCSFL patient as a case report and demonstrate the need for future prospective studies to improve further understanding and management of the condition. This is explored through summarizing the literature on mCSFLs to the present time. A 45-year-old obese female presented with a 5-week history of bilateral clear nasal discharge with no previous surgery or trauma. Initially misdiagnosed as allergic rhinitis, nasal discharge was later confirmed positive for beta-2-transferrin and a CT scan revealed multiple bony areas of skull base erosion. The patient underwent image guided endoscopic sinus surgery with multilayer skull base defect repair of three active CSFLs. Three months post-operatively the patient had no evidence of a recurrence. As mCSFL tend to be sporadic and asymptomatic in nature, it is likely they are underreported. Future prospective studies in the area of mCSFLs that take into consideration demographic and geographical information are needed for more accurate interpretation and better recruitment. Additionally, studies need to ensure longer follow-up time due to the underlying chronicity of increased intracranial pressure (ICP) and investigate whether or not a non-active leak site should be repaired or left alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".