A Population-Based Study of the Long-Term Risk of Infections Associated With Hospitalization in Childhood Cancer Survivors
Bibliographic record
Abstract
PURPOSE: Infections pose a significant risk during therapy for childhood cancer. However, little is known about the risk of infection in long-term survivors of childhood cancer. METHODS: We performed a retrospective observational study of children and adolescents born in Washington State diagnosed with cancer before age 20 years and who survived at least 5 years after diagnosis. Survivors were categorized as having a hematologic or nonhematologic malignancy and were matched to individuals without cancer in the state birth records by birth year and sex with a comparator:survivor ratio of 10:1. The primary outcome was incidence of any infection associated with a hospitalization using diagnostic codes from state hospital discharge records. Incidence was reported as a rate (IR) per 1,000 person-years. Multivariate Poisson regression was used to calculate incidence rate ratios (IRR) for cancer survivors versus comparators. RESULTS: On the basis of 382 infection events among 3,152 survivors and 771 events among 31,519 comparators, the IR of all hospitalized infections starting 5 years after cancer diagnosis was 12.6 (95% CI, 11.4 to 13.9) and 2.4 (95% CI, 2.3 to 2.6), respectively, with an IRR 5.1 (95% CI, 4.5 to 5.8). The survivor IR during the 5- to 10-year (18.1, 95% CI, 15.9 to 20.5) and > 10-year postcancer diagnosis (8.3, 95% CI, 7.0 to 9.7) periods remained greater than comparison group IRs for the same time periods (2.3, 95% CI, 2.1 to 2.6 and 2.5, 95% CI, 2.3 to 2.8, respectively). When potentially vaccine-preventable infections were evaluated, survivors had a greater risk of infection relative to comparators (IRR, 13.1; 95% CI, 7.2 to 23.9). CONCLUSION: Infectious complications continue to affect survivors of childhood cancer many years after initial diagnosis. Future studies are needed to better understand immune reconstitution to determine specific factors that may mitigate this risk.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".