Sumatriptan for Postcraniotomy Headache after Minimally Invasive Craniotomy for Clipping of Aneurysms: A Prospective Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Introduction Postcraniotomy headaches are often underestimated and undertreaded. This study aimed to identify if postoperative administration of sumatriptan after minimally invasive craniotomy for clipping an unruptured aneurysm could reduce postcraniotomy headache and improve the quality of postoperative recovery. Settings and Design Tertiary care center, single-center randomized double-blind placebo-controlled trial. Materials and Methods Patients who complained of postoperative headaches after minimally invasive craniotomy for clipping of unruptured aneurysms were randomized to receive subcutaneous sumatriptan (6 mg) or placebo. The primary outcome was the quality of recovery measured 24 hours after surgery. Secondary outcomes were total opioid use and headache score at 24 hours after surgery. Data were analyzed using a Student's t-test or the chi-square test. Results Forty patients were randomized to receive sumatriptan (n = 19) or placebo (n = 21). Both groups had similar demographics, comorbidities, and anesthesia management. The Quality of Recovery 40 score was higher for patients receiving sumatriptan compared to placebo, however, not statistically significant (173 [156–196] vs. 148 [139–181], p = 0.055). Postoperative opioid use between sumatriptan and placebo was lower, but not significant (5.4 vs. 5.6 mg morphine equivalent, p = 0.71). The severity of headache was also not statistically different between the two groups (5 [4–5] vs. 4 [2–5], p = 0.155). Conclusion In patients undergoing minimally invasive craniotomies for aneurysm clipping, sumatriptan given postoperatively has a nonsignificant trend for a higher quality of recovery. Similarly, there was a nonsignificant trend toward lower postcraniotomy headache scores and opioid scores for the patient given sumatriptan.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".