Using near-infrared spectroscopy to monitor spinal cord oxygenation in the injured spinal cord
Bibliographic record
Abstract
Introduction: Current clinical guidelines recommend augmenting the mean arterial pressure (MAP) in acute spinal cord injury (SCI) patients to increase perfusion and oxygen delivery to the spinal cord, and potentially improve neurologic function. However, it is difficult for clinicians to hemodynamically manage acute SCI patients without real-time physiologic information about the effect of MAP augmentation within the injured cord. In this study, we investigated the utilization of a customized optical sensor, based on near-infrared spectroscopy (NIRS), to non-invasively monitor spinal cord oxygenation during the first week post-injury in a porcine model. Methods: Six Yucatan mini-pigs received a weight-drop T10 contusion-compression injury. A multi-wavelength NIRS system with a custom-made miniaturized sensor was placed directly onto the dura. The spinal cord tissue oxygenation index (TOI) and concentrations of oxygenated, deoxygenated, and total hemoglobin were monitored before and after SCI. To validate the NIRS measures, invasive intraparenchymal (IP) combined PO2/blood flow sensors were inserted into the spinal cord adjacent to the NIRS sensor. Episodes of MAP alteration and hypoxia were performed acutely after injury, 2 days post-injury, and 7 days post-injury to simulate the types of hemodynamic changes SCI patients experience post-injury. Results: Non-invasive NIRS monitoring identified changes in spinal cord oxygenation levels during the MAP alterations. Changes of TOI followed similar patterns of IP-derived oxygenation changes. Conclusion: Our novel NIRS sensor is feasible as a non-invasive technique to monitor real-time changes in spinal cord oxygenation 7 days post-injury in a porcine model of SCI.
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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 | 0.000 |
| 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 | 0.000 |
| 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".