The role of hyperbaric oxygen therapy in the management of perioperative peripheral nerve injury: a scoping review of the literature
Bibliographic record
Abstract
BACKGROUND/IMPORTANCE: Peripheral nerve injury is an uncommon but potentially catastrophic complication of anesthesia and surgery, for which there are limited effective treatment options. Hyperbaric oxygen therapy is a unique medical intervention which improves tissue oxygen delivery and reduces ischemia via exposure to oxygen at supra-atmospheric partial pressures. While the application of hyperbaric oxygen therapy has been evidenced for other medical conditions involving relative tissue ischemia, its role in the management of peripheral nerve injury remains unclear. OBJECTIVE: This scoping review seeks to characterize rehabilitative outcomes when hyperbaric oxygen therapy is applied as an adjunct therapy in the treatment of perioperative peripheral nerve injury. EVIDENCE REVIEW: The review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews, using a systematic screening and extraction process. The search included articles published from database inception until June 11, 2022, which reported clinical outcomes (in both human and non-human models) of peripheral nerve injury treated with hyperbaric oxygen therapy. FINDINGS: A total of 51 studies were included in the narrative synthesis. These consisted of animal (40) and human studies (11) treating peripheral nerve injury due to various physiological insults. Hyperbaric oxygen therapy protocols were highly heterogenous and applied at both early and late intervals relative to the time of peripheral nerve injury. Overall, hyperbaric oxygen therapy was reported as beneficial in 88% (45/51) of included studies (82% of human studies and 90% of animal studies), improving nerve regeneration and/or time to recovery with no reported major adverse events. CONCLUSIONS: Existing data suggest that hyperbaric oxygen therapy is a promising intervention in the management of perioperative peripheral nerve injury, in which tissue ischemia is the most common underlying mechanism of injury, neurological deficits are severe, and treatment options are sparse. This positive signal should be further investigated in prospective randomized clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".