Treatment‐related mortality in newly diagnosed pediatric cancer: a population‐based analysis
Bibliographic record
Abstract
Using a previously developed reliable and valid treatment-related mortality (TRM) definition, our objective was to describe the proportion of children newly diagnosed with cancer experiencing TRM and to identify risk factors for TRM in a population-based cohort. We included children with cancer <19 years diagnosed and treated in Ontario who were diagnosed between 2003 and 2012. Children with cancer were identified using data in a provincial registry. Cumulative incidence of TRM was calculated where progressive disease death was considered a competing event. Among the 5179 children included, 179 had TRM, 478 died of progressive disease, and 4522 were still alive. At 5 years, the cumulative incidence of TRM among the entire cohort was 3.9% (95% confidence interval (CI) 3.3-4.5%). When compared to brain tumor patients, leukemia and lymphoma patients had a significantly higher risk of TRM (hazard ratio (HR) 2.5, 95% CI: 1.6-4.0; P < 0.0001). Infants were at significantly higher risk of TRM across diagnostic groups. Other factors associated with higher risks of TRM were metastatic disease (P < 0.0001), diagnosis prior to 1 January 2008 (P = 0.001), hematopoietic stem cell transplantation (HSCT) (P < 0.0001), and relapse (P < 0.0001). The 5-year cumulative incidence of TRM was 3.9% among newly diagnosed children with cancer. Infants were at higher risk of TRM across diagnostic groups. Other risk factors for TRM were leukemia or lymphoma, metastatic disease, earlier diagnosis year, HSCT, and relapse. Future work should further refine prognostic factors by specific cancer diagnosis to best understand when and how to intervene to improve outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".