Stage at diagnosis and survival by stage for the leading childhood cancers in three populations of <scp>sub‐Saharan</scp> Africa
Bibliographic record
Abstract
The lack of accurate population-based information on childhood cancer stage and survival in low-income countries is a barrier to improving childhood cancer outcomes. In our study, data from three population-based registries in sub-Saharan Africa (Abidjan, Harare and Kampala) were examined for children aged under 15. We assessed the feasibility of assigning stage at diagnosis according to Tier 1 of the Toronto Childhood Cancer Stage Guidelines for patients with non-Hodgkin lymphoma [including Burkitt lymphoma (BL)], retinoblastoma and Wilms' tumour. Patients were actively followed-up, allowing calculation of 3-year relative survival by cancer type and registry. Stage-specific observed survival was estimated. The cohort comprised 381 children, of whom half (n = 192, 50%) died from any cause within 3 years of diagnosis. Three-year relative survival varied by malignancy and location and ranged from 17% [95% confidence interval (CI) = 6%-33%] for BL in Harare to 57% (95% CI = 31%-76%) for retinoblastoma in Kampala. Stage was assigned for 83% of patients (n = 317 of 381), with over half having metastatic or advanced disease at diagnosis (n = 166, 52%). Stage was a strong predictor of survival for each malignancy; for example, 3-year observed survival was 88% (95% CI = 68%-96%) and 13% (4%-29%) for localised and advanced BL, respectively (P < .001). These are the first data on stage distribution and stage-specific survival for childhood cancers in Africa. They demonstrate the feasibility of the Toronto Stage Guidelines in a low-resource setting and highlight the value of population-based cancer registries in aiding our understanding of the poor outcomes experienced by this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".