Implementing the WHO Global Initiative for Childhood Cancer in Morocco: Survival study for the six indexed childhood cancers
Bibliographic record
Abstract
BACKGROUND: In 2018, the World Health Organization (WHO) launched the Global Initiative for Childhood Cancer (GICC). The goal is to achieve a global survival rate of at least 60% for all children with cancer by 2030. Morocco was designated as a pilot country for this initiative. PROCEDURE: This retrospective study included a cohort of children aged 0-15 years, with one of the six indexed cancers (acute lymphoblastic leukemia [ALL], Burkitt lymphoma [BL], Hodgkin lymphoma, retinoblastoma [RB], Wilms tumor or nephroblastoma, low-grade glioma), diagnosed between January 1, 2017 and December 31, 2019 at the six Moroccan Pediatric Hematology and Oncology units. Patients were followed-up until August 31, 2020. The Kaplan-Meier method was used to estimate survival rates, the log-rank test for comparing survival curves, and the Cox model for identifying prognostic factors. RESULTS: Data on 878 patients were included in the study. The most frequently reported cancer type was ALL (n = 383, 43.6%), followed by Wilms tumor (n = 139, 15.8%) and BL (n = 133, 15%). Most patients were less than 5 years of age (n = 446, 50.9%) and the male/female ratio was 1.46. The 1, 2, and 3-year overall survival rates were 80.1%, 73.6%, and 68.2%, respectively. In a multivariable Cox regression model, care center, cancer type, age group, and distance to the care center were statistically significantly associated to survival. Patients aged 10 years and older and patients living more than 100 km from the care center were more likely to die (respectively, HR = 1.39, p = .045 and HR = 1.44, p = .010). CONCLUSION: The reported results represent the baseline for measuring the impact of GICC implementation in Morocco.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".