Exploring medical gas therapy in hemorrhagic stroke treatment: A narrative review
Bibliographic record
Abstract
Hemorrhagic stroke (HS) is a neurological disorder caused by the rupture of cerebral blood vessels, resulting in blood seeping into the brain parenchyma and causing varying degrees of neurological impairment, including intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH). Current treatment methods mainly include hematoma evacuation surgery and conservative treatment. However, these methods have limited efficacy in enhancing neurological function and prognosis. The current challenge in treating HS lies in inhibiting the occurrence and progression of secondary brain damage after bleeding, which is a key factor affecting the prognosis of HS patients. Studies have shown that medical gas therapy is gaining more attention and has demonstrated various levels of neuroprotective effects on central nervous system disorders, such as hyperbaric oxygen, hydrogen sulfide, nitric oxide, carbon monoxide, and other inhalable gas molecules. These medical gas molecules primarily improve brain tissue damage and neurological dysfunction by regulating inflammation, oxidative stress, apoptosis, and other processes. However, many of these medical gasses also possess neurotoxic properties. Therefore, the use of medical gases in HS deserves further exploration and research. In this review, we will elucidate the therapeutic effects and study the advances in medical gas molecules in HS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".