Long-Term Risk of Infections After Treatment of Childhood Leukemia: A Population-Based Cohort Study Using Administrative Health Data
Bibliographic record
Abstract
PURPOSE: Infections are a frequent complication during childhood leukemia treatment. Little is known about the infectious risk in survivors. We compared the relative rate (RR) of infections after treatment completion between pediatric leukemia survivors and the general population. METHODS: We performed a retrospective, population-based cohort study of children diagnosed with leukemia between 1992 and 2015 in Ontario, Canada, who were alive and relapse free 30 days after treatment completion (index date). Leukemia survivors were matched 5:1 with the general population by year of birth, sex, and rural status and stratified by initial treatment, including and excluding hematopoietic stem-cell transplantation (HSCT). The primary outcome was time to infections, as identified using validated diagnostic codes from administrative databases. Individuals were censored at the earliest of death, first relapse, loss to follow-up, or end of study. RESULTS: A total of 2,204 leukemia survivors were included and matched with 11,020 controls. The rate of infections was elevated after treatment completion compared with controls (RR, 1.51; 95% CI, 1.45 to 1.57) and at less than 1 year (RR, 1.77; 95% CI, 1.69 to 1.86); 1 to 4.99 years (RR, 1.66; 95% CI, 1.62 to 1.71), and 5 or more years (RR, 1.29; 95% CI, 1.22 to 1.36) from the index date. Among those whose initial treatment excluded HSCT, the rate remained elevated more than 5 years from the index date (RR, 1.29; 95% CI, 1.23 to 1.35). Infection-related death was significantly increased in leukemia survivors both among the entire cohort (hazard ratio, 149.3; 95% CI, 20.4 to 1,091.9) and among those without HSCT (hazard ratio, 92.7; 95% CI, 12.4 to 690.7). CONCLUSION: A significant association was found between a history of leukemia therapy and an increased risk of infections. Additional study is needed to establish which exposures in patients with leukemia lead to late infections.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".