Awake Craniotomy in Africa: A Scoping Review of Literature and Proposed Solutions to Tackle Challenges
Bibliographic record
Abstract
BACKGROUND: Awake craniotomy (AC) is a common neurosurgical procedure for the resection of lesions in eloquent brain areas, which has the advantage of avoiding general anesthesia to reduce associated complications and costs. A significant resource limitation in low- and middle-income countries constrains the usage of AC. OBJECTIVE: To review the published literature on AC in African countries, identify challenges, and propose pragmatic solutions by practicing neurosurgeons in Africa. METHODS: We conducted a scoping review under Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Review guidelines across 3 databases (PubMed, Scopus, and Web of Science). English articles investigating AC in Africa were included. RESULTS: Nineteen studies consisting of 396 patients were included. Egypt was the most represented country with 8 studies (42.1%), followed by Nigeria with 6 records (31.6%). Glioma was the most common lesion type, corresponding to 120 of 396 patients (30.3%), followed by epilepsy in 71 patients (17.9%). Awake-awake-awake was the most common protocol used in 7 studies (36.8%). Sixteen studies (84.2%) contained adult patients. The youngest reported AC patient was 11 years old, whereas the oldest one was 92. Nine studies (47.4%) reported infrastructure limitations for performing AC, including the lack of funding, intraoperative monitoring equipment, imaging, medications, and limited human resources. CONCLUSION: Despite many constraints, AC is being safely performed in low-resource settings. International collaborations among centers are a move forward, but adequate resources and management are essential to make AC an accessible procedure in many more African neurosurgical centers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".