Optical Assessment of Spinal Cord Tissue Oxygenation Using a Miniaturized Near Infrared Spectroscopy Sensor
Bibliographic record
Abstract
Despite advances in the treatment of acute spinal cord injury (SCI), measures to mitigate permanent neurological deficits in affected patients are limited. Immediate post-trauma hemodynamic management of patients, to maintain blood supply and improve oxygenation to the injured spinal cord, is currently one aspect of critical care which clinicians can utilize to improve neurological outcomes. However, without a way to monitor the response of spinal cord hemodynamics and oxygenation in real time, optimizing hemodynamic management is challenging and limited in scope. This study aims to investigate the feasibility and validity of using a miniaturized multi-wavelength near-infrared spectroscopy (NIRS) sensor for direct transdural monitoring of spinal cord oxygenation in an animal model of acute SCI. Nine Yorkshire pigs underwent a weight-drop T10 contusion-compression injury and received episodes of ventilatory hypoxia and alterations in mean arterial pressure (MAP). Spinal cord hemodynamics and oxygenation were monitored throughout by a non-invasive transdural NIRS sensor, as well as an invasive intraparenchymal sensor as a comparison. NIRS parameters of tissue oxygenation were highly correlated with intraparenchymal measures of tissue oxygenation. In particular, during periods of hypoxia and MAP alterations, changes of NIRS-derived spinal cord oxygenated hemoglobin and tissue oxygenation percentage corresponded well with the changes in spinal cord oxygen partial pressures measured by the intraparenchymal sensor. Our data confirm that during hypoxic episodes and as changes occur in the MAP, non-invasive NIRS can detect and measure real-time changes in spinal cord oxygenation with a high degree of sensitivity and specificity.
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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".