The Effect of Sedation on Intracranial Pressure in Patients with an Intracranial Space-Occupying Lesion: Remifentanil Versus Propofol
Bibliographic record
Abstract
BACKGROUND: In this study, we compared the effect of light sedation with remifentanil versus propofol on intracranial (ICP) and cerebral perfusion pressure (CPP) of patients undergoing stereotactic brain tumor biopsy under regional anesthesia. METHODS: This was a prospective, open-label, randomized, and controlled study. Forty patients undergoing stereotactic brain tumor biopsy under regional anesthesia were randomized into two groups to receive remifentanil or propofol titrated to a level of four on the modified Assessment of Alertness/Sedation Scale. ICP was measured via the biopsy needle. RESULTS: At the targeted level of sedation, the rates of infusion for remifentanil and propofol were, respectively, 4.2 +/- 1.8 microg x kg(-1) x h(-1) and 4.3 +/- 2.5 mg x kg(-1) x h(-1). At the time of ICP measurement, patients in the remifentanil group had a slower respiratory rate (11/min +/- 3 vs 15 per min +/- 3, P = 0.0001) and a higher PCO2 (48.3 +/- 6.2 mm Hg vs 43.1 +/- 5.5 mm Hg, P = 0.009) than patients in the propofol group. The mean was similar for both groups, 19.0 +/- 11.9 mm Hg vs 16.4 +/- 11.1 mm Hg for remifentanil and propofol, respectively (P = 0.48). Higher mean arterial blood pressure in the remifentanil group (101.1 +/- 13.7 mm Hg vs 85.8 +/- 12.7 mm Hg, P = 0.0008) resulted in a higher CPP than the propofol group: 82.0 +/- 19.0 mm Hg vs 69.5 +/- 17.0 +/- 19.0 mm Hg (P = 0.03). CONCLUSION: Light sedation with remifentanil does not result in a higher ICP than propofol in patients undergoing stereotactic brain tumor biopsy. CPP might be better preserved with remifentanil.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".