Perspectives on Analgesia for Craniotomy: A Survey of Anesthetic Practices
Bibliographic record
Abstract
BACKGROUND: This study aimed to compare analgesic practices for patients undergoing craniotomy in high-income countries (HICs) and low-income and middle-income countries (LMICs), focusing on variations in medication use and techniques. METHODS: An English-language and Spanish-language electronic survey was sent to over 300 anesthesiologists in 35 countries from March 22 to May 19, 2024, to gather data on analgesia for craniotomy patients. Anonymous responses through REDCap were analyzed as a whole and by income category (HICs and LMICs). RESULTS: We received 328 responses (105 HICs, 221 LMICs, and 2 missing locations). Acetaminophen was used by 78% of respondents (HIC: 82%, LMIC: 76%), with low nonavailability in both groups (0.95% HICs, 4.98% LMICs). Fentanyl boluses were used in 57% of cases (HIC: 60%, LMIC: 55%). Incisional local anesthesia was administered in 51% (HIC: 52%, LMIC: 50%), with minimal nonavailability (1.9% HIC, 1.4% LMIC). The use of a remifentanil infusion was more common in HICs (64%) than LMICs (31%), where nonavailability was significantly higher (43.89% vs. 7.62% HICs). Scalp blocks were used by 15% of HICs and 43% of LMICs. Craniotomy indication influenced the choice of analgesia for 61% of respondents. CONCLUSIONS: Analgesic practices for craniotomy vary significantly between HICs and LMICs, primarily due to medication availability. Global guidelines should consider resource differences to improve postoperative pain management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".