Baseline characteristics and outcomes of children with cancer in the English‐speaking Caribbean: A multinational retrospective cohort
Bibliographic record
Abstract
BACKGROUND: English-speaking Caribbean (ESC) childhood cancer outcomes are unknown. PROCEDURE: Through the SickKids-Caribbean Initiative (SCI), we established a multicenter childhood cancer database across seven centers in six ESC countries. Data managers entered patient demographics, disease, treatment, and outcome data. Data collection commenced in 2013, with retrospective collection to 2011 and subsequent prospective collection. RESULTS: A total of 367 children were diagnosed between 2011 and 2015 with a median age of 5.7 years (interquartile range 2.9-10.6 years). One hundred thirty (35.4%) patients were diagnosed with leukemia, 30 (8.2%) with lymphoma, and 149 (40.6%) with solid tumors. A relative paucity of children with brain tumors was seen (N = 58, 15.8%). Two-year event-free survival (EFS) for the cohort was 48.5% ± 3.2%; 2-year overall survival (OS) was 55.1% ± 3.1%. Children with acute lymphoblastic leukemia (ALL) and Wilms tumor (WT) experienced better 2-year EFS (62.1% ± 6.4% and 66.7% ± 10.1%), while dismal outcomes were seen in children with acute myeloid leukemia (AML; 22.7 ± 9.6%), rhabdomyosarcoma (21.0% ± 17.0%), and medulloblastoma (21.4% ± 17.8%). Of 108 deaths with known cause, 58 (53.7%) were attributed to disease and 50 (46.3%) to treatment complications. Death within 60 days of diagnosis was relatively common in acute leukemia [13/98 (13.3%) ALL, 8/26 (30.8%) AML]. Despite this, traditional prognosticators adversely impacted outcome in ALL, including higher age, higher white blood cell count, and T-cell lineage. CONCLUSIONS: ESC childhood cancer outcomes are significantly inferior to high-income country outcomes. Based on these data, interventions for improving supportive care and modifying treatment protocols are under way. Continued data collection will allow evaluation of interventions and ensure maximal outcome improvements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".