Central neurogenic hyperventilation in conscious patients due to CNS neoplasm: a case report and review of the literature on treatment
Bibliographic record
Abstract
BACKGROUND: Central neurogenic hyperventilation (CNH) is increasingly reported in conscious patients with a CNS neoplasm. We aimed to synthesize the available data on the treatment of this condition to guide clinicians in their approach. METHODS: We describe the case of a 39-year-old conscious woman with CNH secondary to glioma brainstem infiltration for whom hyperventilation was aborted with hydromorphone, dexamethasone, and brainstem radiotherapy. We then performed a review of the literature on the treatment of CNH in conscious patients due to a CNS neoplasm. RESULTS: A total of 31 studies reporting 33 cases fulfilled the selection criteria. The underlying neoplasm was lymphoma in 15 (45%) and glioma in 13 (39%) patients. Overall, CNH was aborted in 70% of cases. Opioids and sedatives overall seemed useful for symptom relief, but the benefit was often of short duration when the medication was administered orally or subcutaneously. Methadone and fentanyl were successful but rarely used. Chemotherapy was most effective in patients with lymphoma (89%), but not glioma (0%) or other neoplasms (0%). Patients with lymphoma (80%) and other tumors (100%) responded to radiotherapy more frequently than patients with glioma (43%). Corticosteroids were moderately effective. Subtotal surgical resection was successful in the 3 cases for which it was attempted. CONCLUSION: Definitive treatment of the underlying neoplasm may be more successful in aborting hyperventilation. Variable rates of palliation have been observed with opioids and sedatives. Treatment of CNH is challenging but successful in a majority of cases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".