Current childhood cancer survivor long-term follow-up practices in South Africa
Bibliographic record
Abstract
Background: The number of childhood cancer survivors (CCSs) is increasing due to improved survival. Most suffer at least one treatment-related late effect, even decades after treatment, thus lifelong long-term follow-up (LTFU) care is a necessity. Currently no standardized LTFU programme for CCSs exists in South Africa. Study purpose: This study investigated current LTFU care of CCSs in South Africa. Methods: A survey was conducted amongst 31 South African paediatric oncologists using the SurveyMonkeyTM online tool. Information obtained included: training/experience, LTFU practices, late effects knowledge and opinion regarding the importance of a standardized LTFU programme. Results: The response rate was 74% (23/31). Respondents had an average of 9 years’ experience. All (22/23; 96%) regarded LTFU as important. Only half (12/23; 52%) discussed late effects at diagnosis. Infertility and second malignancy risks were discussed by a third. Less than half (48%) used LTFU guidelines; the majority (9/11; 82%) adjusted them to the local context. Most survivors were followed by a paediatric oncologist (17/23; 74%). About half of respondents (47.8%) shared LTFU with colleagues in private practice (50%), secondary (66.7%) or primary care facilities (25%). Almost half of respondents (10/23; 43.5%) regarded their late effects knowledge and LTFU experience as good, 8/23 (34.8%) as adequate and 3/23 (13%) as inadequate. All agreed that a national LTFU programme would be very important (87%) or important (13%). Almost half of the respondents (48%) understood what a Survivorship Passport was. Conclusion: It is essential to develop a national standardized LTFU programme for CCSs in South Africa to ensure appropriate care for all survivors.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".