P13.01.A Challenges and Solutions for Establishing a CNS Tumor Registry in Africa
Bibliographic record
Abstract
Abstract Background CNS tumor registries (CTR) has evolved to a key tool for data collection, evaluation of diagnostic and treatment of patients suffering from tumors of the central nervous system (tCNS) in the U.S, but CTR in Africa does not yet exist. In comparison to high-income countries (HIC), many low- and middle-income countries (LMIC) do not yet have national or central CNS tumor registries. Furthermore, appropriate diagnostic steps like MRI and pathological analysis are still scarce in many LMIC. Improving the availability of CTR in resource- limited regions could allow better understanding of some specificities of tCNS including incidence, prevalence, mortality and morbidity. However, CTR, MRI and pathological analysis tend to be costly and thus difficult to implement in the LMIC setting. Material and Methods A review of the current body of literature on tCNS in Africa was conducted using multiple scientific online data bases. Search terms included ,,CNS tumor registry,” “developing countries,” “low and middle income,” and other related terms as starting point for future initiatives. Results It was found that more than 1,3 billion people residing in Africa lack access to a continental CTR. There is no well established standards for reporting tCNS. Most tCNS are still underreported in many countries of Africa. The exact burden of tCNS in Africa is unknown. Although many successful, long-term, initiatives for international neurological and neurosurgical collaborations are published, any CTR of Africa similarly to the Central Brain Tumor Registry of the United States (CBTRUS) is lacking. Conclusion: Disparities in access to care for patients suffering from tCNS have been well published but well established solutions are still under investigations. Partnerships between centers in LMIC and HIC are making progress to better understand the burden of disease in LMIC and to create context-specific solutions for practice in the LMIC setting. Collaboration between the World Health Organisation, national centers for disease control in Africa, departments of neuroscience in LMIC and well established registries like the CBTRUS as well as other interested groups could be meaningful strategical steps to be initiated for the establishment of CTR in Africa. A CTR for Africa could lead to better comprehension of tCNS in Africa, thus facilitate prevention, diagnostic, treatment and research.
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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 | 0.034 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".