Post-craniotomy headache and botulinum toxin A: A systematic review of case reports and case series
Bibliographic record
Abstract
Background Post-craniotomy headache (PCH) is a common, often debilitating complication with limited treatment options and unclear pathophysiology. While botulinum toxin A (BoNT-A) is effective for various headache disorders, its use in PCH is underexplored. This systematic review examines case reports and series on BoNT-A's efficacy and safety for PCH. Methods A systematic search of PubMed, Scopus, and Web of Science was conducted in February 2025 using relevant keywords. Case reports and series on BoNT-A treatment for PCH were included, while unrelated studies, reviews, and incomplete abstracts were excluded. Data on patient characteristics, treatment protocols, efficacy, and adverse events were extracted. Results Five case series published up to 2025 report on 15 patients from France, Canada, and the United States. Each study enrolled only three or four patients, all with persistent PCH unresponsive to standard analgesics. BoNT-A regimens differed widely, ranging from 15 to 165 U, and included single versus repeated sessions, as well as injection sites such as the temporalis muscle, incision margins, and cranial suture lines, highlighting the lack of a standardized protocol. Eleven patients achieved 75–100% pain relief within 10–15 days, with therapeutic effects persisting for several weeks to over 5 years. Many also demonstrated improvements in daily functioning and a reduction in analgesic consumption. No serious adverse events were reported, supporting BoNT-A as a safe and promising treatment for PCH. Conclusion BoNT-A is a well-tolerated and effective option for patients with refractory PCH, offering substantial pain relief and functional improvement. However, given the reliance on small-scale studies, larger clinical trials are needed to confirm its efficacy and establish standardized treatment protocols.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".