Spinal cord perfusion pressure predicts neurologic recovery in acute spinal cord injury
Bibliographic record
Abstract
<h3>Objective:</h3> To determine whether spinal cord perfusion pressure (SCPP) as measured with a lumbar intrathecal catheter is a more predictive measure of neurologic outcome than the conventionally measured mean arterial pressure (MAP). <h3>Methods:</h3> A total of 92 individuals with acute spinal cord injury were enrolled in this multicenter prospective observational clinical trial. MAP and CSF pressure (CSFP) were monitored during the first week postinjury. Neurologic impairment was assessed at baseline and at 6 months postinjury. We used logistic regression, systematic iterations of relative risk, and Cox proportional hazard models to examine hemodynamic patterns commensurate with neurologic outcome. <h3>Results:</h3> We found that SCPP (odds ratio 1.039, <i>p</i> = 0.002) is independently associated with positive neurologic recovery. The relative risk for not recovering neurologic function continually increased as individuals were exposed to SCPP below 50 mm Hg. Individuals who improved in neurologic grade dropped below SCPP of 50 mm Hg fewer times than those who did not improve (<i>p</i> = 0.012). This effect was not observed for MAP or CSFP. Those who were exposed to SCPP below 50 mm Hg were less likely to improve from their baseline neurologic impairment grade (<i>p</i> = 0.0056). <h3>Conclusions:</h3> We demonstrate that maintaining SCPP above 50 mm Hg is a strong predictor of improved neurologic recovery following spinal cord injury. This suggests that SCPP (the difference between MAP and CSFP) can provide useful information to guide the hemodynamic management of patients with acute spinal cord injury.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".