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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".