Pain Management in Post-Craniotomy Patients: A Survey of Canadian Neurosurgeons
Bibliographic record
Abstract
INTRODUCTION: Despite the growing recognition for analgesic needs in post-craniotomy patients, this remains a poorly studied area in neurological surgery. The class and regimen of analgesia that is most suitable for these patients remains controversial. The objective of this study is to examine the current beliefs and practices of Canadian neurosurgeons when managing post-craniotomy pain. METHODS: A survey was sent to all practicing Canadian neurosurgeons to examine the following aspects of analgesia in craniotomy patients: type of analgesics used, common side effects encountered, satisfaction with current regimen and the rationale for their practice. RESULTS: Of 156 potential respondents, 103 neurosurgeons (66%) completed the survey. Codeine (59%) was the most prescribed first line analgesic followed by morphine (38%). The use of a second-line opioid was significantly higher among codeine prescribers compared to morphine, 53% compared to 28% (p < 0.001). Nausea, constipation and neurologic depression were reported as common side effects by 76%, 66% and 27% of respondents respectively. Of the respondents, 90% reported a high level of satisfaction with their current choice of analgesia; nonetheless, they predominantly described their practice as personal preference or protocol driven rather than evidence-based. CONCLUSIONS: Codeine - a weak opioid - is the most common first-line analgesic prescribed to craniotomy patients. This practice is associated with substantially increased reliance on potent opioids for rescue analgesia. Whether novel regimens can provide optimal pain control while minimizing neurologic and gastrointestinal side effects remains to be addressed by future trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".