Patient Satisfaction with Awake Craniotomy for Tumor Surgery: A Comparison of Remifentanil and Fentanyl in Conjunction with Propofol
Bibliographic record
Abstract
In Brief In this study we compared the effectiveness of the use of remifentanil to fentanyl in conjunction with propofol in providing conscious sedation for awake craniotomy for tumor surgery and to assess patient satisfaction with both techniques. The ability to maintain appropriate levels of sedation, adequate analgesia, and hemodynamic stability was assessed in 50 patients randomized to receive either fentanyl or remifentanil. All complications were documented. Patients were interviewed at 1 h, 4 h, and 24 h after surgery to note their recall of procedure and pain and their overall satisfaction. There were no differences in sedation and pain scores or in hemodynamic and respiratory variables between the two groups. The incidence of intraoperative complications was not different (fentanyl, 14; remifentanil, 16). Respiratory complications occurred in 9 (18%) patients (fentanyl 6, remifentanil 3). The recall and satisfaction scores were not different; 93% of all patients were completely satisfied at all interview times. The use of remifentanil infusion in conjunction with propofol is a good alternative to fentanyl and propofol for conscious sedation for the awake craniotomy and these techniques are both well accepted by the patient. IMPLICATIONS: The use of remifentanil and propofol when compared with fentanyl and propofol was not different in the ability to maintain adequate sedation, analgesia, and hemodynamic stability or in the incidence of complications during awake craniotomy for tumor surgery. Patient satisfaction was high for both techniques.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".