Clinical Considerations for Implanted Neurological Devices in Patients Undergoing Hyperbaric Oxygen Therapy: A Case Report and Review of Manufacturer Guidelines
Bibliographic record
Abstract
Patients with implanted medical devices are increasingly referred for hyperbaric oxygen therapy (HBOT), and the safety of exposing some of these devices to hyperbaric environments has not previously been explored. There is a paucity of evidence surrounding the management of implanted neurological devices such as neurostimulators and intrathecal drug delivery (IDD) pumps in the context of HBOT. However, these devices can be expected to harbor unique risks; for example, vacant space in the reservoir of an implanted IDD pump may change in pressure and volume during the compression and decompression phases of HBOT, resulting in a damaged or dysfunctional device. We present the case of a 27-year-old woman with cerebral palsy referred for HBOT to manage a necrotizing soft tissue infection cultured from a dehiscent abdominal wound at the previous implantation site of an intrathecal baclofen pump. An HBOT protocol was ultimately chosen in partnership with the patient and her family, but treatment was not performed due to a paucity of evidence that the implanted IDD pump could safely withstand hyperbaric exposure. In this review, we have synthesized manufacturer recommendations regarding the management of implanted neurological devices before, during, and after HBOT to inform future decision-making in this setting. Among these recommendations, we highlight that neurostimulators should be switched off for the duration of HBOT and implanted pumps should be refilled prior to each treatment session to minimize empty reservoir space.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".