Hyperbaric Oxygen for Radiation Necrosis of the Brain
Bibliographic record
Abstract
INTRODUCTION: Hyperbaric oxygen therapy (HBOT) shows promising results in treating radionecrosis (RN) but there is limited evidence for its use in brain RN. The purpose of this study is to report the outcomes of using HBOT for symptomatic brain RN at a single institution. METHODS: This was a retrospective review of patients with symptomatic brain RN between 2008 and 2018 and was treated with HBOT. Demographic data, steroid use, clinical response, radiologic response and toxicities were collected. The index time for analysis was the first day of HBOT. The primary endpoint was clinical improvement of a presenting symptom, including steroid dose reduction. RESULTS: Thirteen patients who received HBOT for symptomatic RN were included. The median time from last brain radiation therapy to presenting symptoms of brain RN was 6 months. Twelve patients (92%) had clinical improvement with median time to symptom improvement of 33 days (range 1-109 days). One patient had transient improvement after HBOT but had recurrent symptomatic RN at 12 months. Of the eight patients with evaluable follow-up MRI, four patients had radiological improvement while four had stable necrosis appearance. Two patients had subsequent deterioration in MRI appearances, one each in the background of initial radiologic improvement and stability. Median survival was 15 months with median follow-up of 10 months. Seven patients reported side effects attributable to HBOT (54%), four of which were otologic in origin. CONCLUSIONS: HBOT is a safe and effective treatment for brain RN. HBOT showed clinical and radiologic improvement or stability in most patients. Prospective studies to further evaluate the effectiveness and side effects of HBOT are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".