Late mortality from other diseases following childhood cancer in Australia and the impact of intensity of treatment
Bibliographic record
Abstract
Abstract Background People who receive treatment for cancer during childhood often experience subsequent complications of therapy, known as late effects, which can lead to an increased risk of death. Procedure Using deidentified population‐based data from the Australian Childhood Cancer Registry for children aged 0‐14 diagnosed with cancer during the period 1983‐2011 and who survived for a minimum of 5 years, we examined disease‐related deaths (other than cancer recurrence or second primary cancers) that occurred up to 31 December 2016. Risk of death relative to the general population was approximated using standardised mortality ratios (SMRs). Treatment received was stratified according to the intensity of treatment rating, version 3 (ITR‐3). Results During the study period, 82 noncancer disease‐related deaths were recorded among 13 432 childhood cancer survivors, four times higher than expected (SMR = 4.43, 95% CI = 3.57‐5.50). A clear link to treatment intensity was observed, with the relative risk of noncancer disease‐related mortality being twice as high for children who underwent ‘most intensive’ treatment (SMR = 5.94, 95% CI = 3.69‐9.55) compared to the ‘least intensive’ treatment group (SMR = 2.98, 95% CI = 1.42‐6.24; Ptrend = .01). Thirty‐year cumulative mortality from noncancer disease‐related deaths was estimated at 1.4% (95% CI = 1.1‐1.9) after adjusting for competing causes of death such as cancer, accidents, or injuries. Conclusions Although childhood cancer survivors are at increased relative risk of death from noncancer diseases, particularly those who undergo more intensive treatment, the cumulative mortality within 30 years of diagnosis remains small. Knowledge of late effects can guide surveillance of survivors and treatment modification, without wanting to compromise the high rates of survival.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".