Successful treatment of neurologic injury after complex spinal surgery with hyperbaric oxygen therapy: a case report
Bibliographic record
Abstract
Background: Neurologic injury is relatively common in the context of spinal surgery, and is often treated with physiotherapy, pharmacotherapy, or surgical intervention. Emerging evidence supports a possible role for hyperbaric oxygen therapy (HBOT) in the treatment of peripheral and spinal nerve injuries. We describe the successful use of HBOT in improving neurologic recovery after complex spine surgery with new-onset postoperative unilateral foot drop. Case Description: A 50-year-old woman was found to have new right-sided foot drop and L2-S1 motor deficits following complex thoracolumbar revision spinal surgery. She received standard conservative management for a provisional diagnosis of acute traumatic nerve ischemia, but demonstrated no neurologic improvement. On postoperative day four, after other avenues of treatment were exhausted, she was referred for HBOT. The patient received a total of twelve sessions of HBOT at 2.0 absolute atmospheres (ATA) of pressure, for 90 minutes (including two air breaks) per session, before transfer to a rehabilitation facility. Conclusions: The patient displayed marked neurologic improvement after the first hyperbaric session, and further recovery thereafter. She concluded therapy with a significantly improved range of motion and lower limb power, ability to ambulate, and pain control. HBOT was associated with a rapid, sustained improvement when applied in this case as a salvage therapy for persistent postoperative neurologic deficit. Mounting evidence supports the consideration of hyperbaric therapy as a standard adjunct treatment for traumatic neurologic 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".