Evaluating the current neuro-oncology capacity in Sub-Saharan Africa: A questionnaire-based survey
Bibliographic record
Abstract
Background: Central nervous system (CNS) tumors are a significant cause of morbidity and mortality in Sub-Saharan Africa (SSA). This project aimed to assess and map out the current neuro-oncology capacity in SSA, brain tumor registries, and biobanks. Methods: This cross-sectional study utilized an online survey to gather data from healthcare professionals involved in CNS tumor care across SSA through the Society for Neuro-Oncology SSA (SNOSSA). The survey captured information on the availability of neuro-oncology practitioners, neuropathology, molecular diagnostics, cancer registries, and biobanking. Results: A total of 145 respondents representing 70 healthcare institutions across 22 countries participated. Of the 70 institutions, specialties included neurosurgery (89%, 62/70), anatomic pathology (74%, 52/70), and medical oncology (71%, 50/70). For radiologic evaluation, 64% (45/70) of responding institutions reported having both CT and MRI. Access to molecular testing through immunohistochemistry or next-generation sequencing was available in 46% (32/70) of responding institutions. Multidisciplinary tumor boards were available for CNS cases in 86% (60/70) of responding institutions. Brain tumor-specific registries were available in 29% (20/70) of responding institutions, and 10% (7/70) maintained brain tumor biorepositories. Conclusion: This study demonstrates that SSA has substantial potential in neuro-oncology care practice capacity and facilities. The most significant gaps were in neuropathology and molecular testing, which makes widespread implementation of the WHO 2021 diagnosis challenging. Understanding the prevalence of CNS tumor subtypes in SSA is difficult due to the low number of brain tumor registries and biobanks.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".