Symptom Predictors of Cerebrospinal Fluid Leaks
Bibliographic record
Abstract
BACKGROUND: Spinal cerebrospinal fluid (CSF) leaks, which are considered a cause of intracranial hypotension, generally do not cause any local symptoms. Although symptoms are key elements for further evaluation, few studies have examined symptom predictors of intracranial hypotension. The aim of this study was to determine what symptoms are predictors of CSF leaks in patients suspected of intracranial hypotension. METHODS: We performed radionuclide cisternography in 207 consecutive patients suspected of intracranial hypotension. Intracranial hypotension was suspected when a patient had a history of minor trauma and complained about uncontrolled headache, cranial nerve dysfunction, autonomic dysfunction, or higher brain dysfunction. The leakage of CSF was defined as direct signs of tracer leak into the spinal epidural space or early accumulation of the tracer in the urinary bladder. We obtained information on 16 symptoms commonly reported in previous studies. RESULTS: CSF leaks were observed in 154 cases (74%). Back pain, limb pain, and limb numbness were inversely associated with CSF leaks (p = 0.042, p = 0.045, and p = 0.006, respectively). In logistic regression analysis, diplopia was a positive predictor of CSF leaks (odds ratio [OR], 6.53; 95% confidence interval [CI], 1.49 to 28.51), whereas limb numbness was a negative predictor (OR, 0.38; 95% CI, 0.17 to 0.84). Of the 21 patients in whom diplopia was present and limb numbness was absent, 20 had CSF leaks (specificity, 98%; positive predictive value, 95%). CONCLUSION: Some symptoms may be helpful in the diagnosis of CSF leaks in patients suspected of intracranial hypotension.
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.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".