Hyperbaric oxygen therapy for spinal cord ischaemia after complex aortic repair — a retrospective review
Bibliographic record
Abstract
BACKGROUND: Complex aortic repair (CAR) carries high rates of debilitating postoperative complications, including spinal cord injury. The rate of spinal cord deficits post-CAR is approximately 10%, with permanent paraplegia in 2.9% and paraparesis in 2.4% of patients. Treatment options are limited. Rescue therapies include optimization of spinal cord perfusion and oxygen delivery by mean arterial pressure augmentation (> 90 mm Hg), cerebrospinal fluid drainage, and preservation of adequate haemoglobin concentration (> 100 g L⁻¹). Hyperbaric oxygen therapy (HBOT) has been described in several case reports as part of the multimodal treatment for spinal cord ischemia. HBOT has been used in our centre as adjunct rescue treatment for patients with spinal cord injury post-CAR that were refractory to traditional medical management, and we aimed to retrospectively review these cases. METHODS: After Research Ethics Board approval, we performed a retrospective review of all post-CAR patients who developed spinal cord injury with severe motor deficit and were treated with HBOT at our institution since 2013. RESULTS: Seven patients with spinal cord injury after CAR were treated with HBOT in addition to traditional rescue therapies. Five patients showed varying degrees of recovery, with two displaying full recovery. One developed oxygen-induced seizure, medically treated. No other HBOT-related complications were noted. CONCLUSIONS: Our retrospective study shows a potential benefit of hyperbaric oxygen therapy on neurological outcome in patients who developed spinal cord injury after CAR. Prospective research is needed to understand the role, efficacy, benefits and risks of HBOT in this setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".