P.227 Investigating the changes in ITP after CSF drainage in patients with acute traumatic SCI: Results from a Quaternary Spinal Care Center
Bibliographic record
Abstract
Background: Mean arterial pressure augmentation is one current established practice for management of patients with SCI. We present the first data investigating the effectiveness of Intrathecal Pressure (ITP) reduction through CSF drainage (CSFD) in managing patients with acute traumatic SCI at a large academic center. Methods: Data from 6 patients with acute traumatic SCI were included. A lumbar intrathecal catheter was used to monitor ITP and volume of CSFD. CSFD was performed and recorded hourly. ITP recordings were collected hourly and the change in ITP was calculated (hour after minus before CSFD). 369 data points were collected and change in ITP was plotted against volume of CSFD. Results: Data across all patients showed variability in the ITP over time without a significant trend (slope=0.016). We found no significant change in ITP with varying amounts of CSFD (slope=0.007, r2=0.00, p=0.88). Changes in ITP were not significantly different across groups of CSFD but the variation in the data decreased with increasing levels of CSFD. Conclusions: We present the first known data on changes in ITP with varying degrees of CSFD in patients with acute traumatic SCI. These results may provide insight into the complexity of ITP changes in patients post-injury and help inform future SCI management.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".