Early death and treatment‐related mortality: A report from SUCCOUR ‐ Supportive Care for Children with Cancer in Africa
Bibliographic record
Abstract
BACKGROUND: Deaths during paediatric cancer treatment are common in Africa. It is often difficult to distinguish between treatment-related and disease-related causes. To prevent these deaths, it is important to study them and identify the cause. The Supportive Care for Children with Cancer in Africa (SUCCOUR) programme enabled a study with the objective to identify the reasons for early death during treatment. METHODS: We conducted a multicentre prospective, observational cohort study in sub-Saharan Africa. Children younger than 16 years with newly diagnosed cancer treated with curative intent were included from 1 September 2019 until 30 March 2020. Data were abstracted in real time by trained personnel using standardised case report forms. The treating clinician's assessment of the cause of death and signs, symptoms and laboratory values of patients who died during the first 3 months of treatment (early death) were documented. RESULTS: We included 252 patients (median age 6.0, range 0.2-15.0 years, 54% male). The most common cancer was Burkitt lymphoma (63/252, 25%). Fifteen percent of patients (37/252) died during the first 3 months of treatment. Of these 37 patients, 33 (89%) died of a treatment-related cause. Treatment-related mortality of all patients in the first 3 months of treatment was 13% (33/252). CONCLUSION: Fifteen percent of patients had an early death during treatment and 13% had a treatment-related death. This suggests the need to improve supportive care. Implementation of supportive care pathways adapted to local circumstances may be helpful.
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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 | 0.003 |
| 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 | 0.001 |
| 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".