Does Hyperventilation Improve Operating Condition During Supratentorial Craniotomy? A Multicenter Randomized Crossover Trial
Bibliographic record
Abstract
BACKGROUND: Hyperventilation has been an integral, but poorly validated part of neuroanesthetic practice. We conducted a two-period, crossover, randomized trial to evaluate surgeon-assessed brain bulk and measured intracranial pressure (ICP) in patients undergoing craniotomy for removal of supratentorial brain tumors during moderate hypocapnia or normocapnia. METHODS: Two-hundred and seventy-five adult patients with supratentorial brain tumors were randomized to one of two treatment sequences: hyperventilation (arterial carbon dioxide tension, PaCO2 = 25 +/- 2 mm Hg) followed by normoventilation (PaCO2 = 37 +/- 2 mm Hg) or normoventilation followed by hyperventilation. Ventilation and end-tidal CO2 tension were kept constant for 20 min. Patients were also randomly assigned to receive a propofol infusion or isoflurane anesthesia. At the end of each study period, subdural ICP was measured and the neurosurgeon, blinded to the treatment group, was asked to rate the brain bulk using a four-point scale. RESULTS: Using a generalized estimation equation model, we found that hyperventilation decreased the risk of increased brain bulk by 45%, P = 0.004, 95% confidence intervals 22% to 61%, and the number needed to treat was 8. The mean (+/-SD) ICP during hyperventilation, 12.3 +/- 8.1 mm Hg, was lower than that during normoventilation, 16.2 +/- 9.6 mm Hg, P < 0.001. Anesthetic regimen did not affect brain bulk assessment or ICP. CONCLUSIONS: In patients with supratentorial brain tumors, intraoperative hyperventilation improves surgeon-assessed brain bulk which was associated with a decrease in ICP.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".