Importance of Frontal Horn Ratio and Optimal CSF Drainage in the Treatment of Very Low-Pressure Hydrocephalus
Bibliographic record
Abstract
Introduction Unlike low-pressure hydrocephalus, very low pressure hydrocephalus (VLPH) is a rarely reported clinical entity previously described to be associated with poor outcomes and to be possibly refractory to treatment with continued cerebrospinal fluid (CSF) drainage at subatmospheric pressures.1, 2 We present four cases of VLPH following resection of suprasellar lesions and hypothesize that untreatable patients can be identified early, thereby avoiding futile prolonged external ventricular drainage in ICU. Methods We performed a retrospective chart review of four cases of VLPH encountered between 2007 and 2015 in two different institutions and practices and tried to identify factors contributing to successful treatment. We hypothesized that normalization of frontal horn ratio (FHR), optimization of volume of CSF drained, and avoidance of fluid shifts would contribute to improved Glasgow Coma Score (GCS). We examined fluid shifts by studying net fluids shifts and serum levels of sodium, urea, and creatinine. We used Pearson and Spearman correlations to identify measures that would correlate with improved GCS. Results Our study reveals that improving GCS is positively correlated with decreased FHR and increased CSF drainage within an optimal range. The most important determinant of good outcome is retention of brain viscoelasticity as evidenced by restoration and maintenance of good GCS score despite fluctuations in FHR. Conclusion Futile prolonged subatmospheric drainage can be avoided by declining to continue treatment in patients who have permanently altered brain compliance secondary to unsealed CSF leaks, irremediable ventriculitis, and who are therefore unable to sustain an improved neurologic examination.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".