Evaluation of treatment-related mortality among paediatric cancer deaths: a population based analysis
Bibliographic record
Abstract
BACKGROUND: Objectives were to describe the proportion of deaths due to treatment-related mortality (TRM) and to identify risk factors and probable causes of TRM among paediatric cancer deaths in a population-based cohort. METHODS: We included children with cancer ⩽18 years diagnosed and treated in Ontario who died between January 2003 and December 2012. Deaths were identified using a provincial registry, the Pediatric Oncology Group of Ontario Networked Information System. Probable causes of TRM were described. RESULTS: Among the 964 deaths identified, 821 were included. The median age at diagnosis was 6.6 years (range 0-18.8) and 51.8% had at least one relapse. Of the deaths examined, TRM occurred in 217/821 (26.4%) while 604/821 (73.6%) were due to progressive cancer. Deaths from TRM did not change over time. Using multiple regression, younger age, leukaemia diagnosis and absence of relapse were independently positively associated with TRM. The most common probable causes of TRM were respiratory, infection and haemorrhage. CONCLUSIONS: TRM was responsible for 26.4% of deaths in paediatric cancer. Underlying diagnosis, younger age and absence of relapse were associated with TRM and causes of TRM differed by diagnosis group. Future work should evaluate TRM rate and risk factors among newly diagnosed cancer patients.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".