High prevalence of long-term and late effects in a South African childhood cancer survivor cohort
Bibliographic record
Abstract
Purpose We documented the prevalence of late effects in a South African childhood cancer survivor (CCS) cohort. Patients and Methods CCSs at Tygerberg Hospital, Cape Town, were evaluated for clinical abnormalities, whereafter late effects were identified, graded according to the Common Terminology Criteria for Adverse Events (CTCAE), and classified as significant or insignificant. Results The cohort comprised 160 CCSs (median age 13 years (interquartile range 9.9 – 17.8 years); follow-up period eight years (range 5 - 37.2 years)). There were 89 (55.6%) hematological and 71 (44.4%) solid malignancies. Most CCSs (146/160; 91.3%) had at least one late effect; the majority were of Grade 1 CTCAE severity (73.7%). Common late effects were gastrointestinal (13.3%), metabolic (12.9%), hematological (9.2%), musculoskeletal (9.1%), and neurological (8.8%) disorders. Significant risk factors for late effects were cancer diagnosis (p = 0.005), chemotherapy (moderate intensity [incidence rate ratio (IRR) 1.84; p=0.036]; high intensity [IRR 2.8; p=0.001]), and radiotherapy (IRR 1.44) (p = < 0.001). Late effects severity was significantly associated with radiotherapy (IRR 1.54; p=0.004). Solid tumor survivors were more likely to develop Grade 2 (IRR 2.4; p=< 0.001) and 3 late effects (IRR 2.8; p=0.011). Conclusion This is the first study of a CCS cohort in South Africa. Most CCSs developed mild or moderate long-term and late effects, significantly associated with cancer diagnosis, chemotherapy intensity, and radiotherapy. It is crucial to develop long-term surveillance plans for CCSs in South Africa to ensure early detection of late effects.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".