Infections among long‐term survivors of childhood and adolescent cancer: A report from the Childhood Cancer Survivor Study
Bibliographic record
Abstract
BACKGROUND: Little is known about infections among adult survivors of childhood cancer. The authors report the occurrence of infections and risk factors for infections in a large cohort of survivors of childhood cancer. METHODS: The Childhood Cancer Survivor Study cohort was used to compare incidence rates of infections among 12,360 5-year survivors of childhood cancer with the rates of 4023 siblings. Infection-related mortality of survivors was compared with that of the US population. Demographic and treatment variables were analyzed using Poisson regression to determine the rate ratios (RRs) and corresponding 95% confidence intervals (CIs) for associations with infectious complications. RESULTS: Compared with the US population, survivors were at an increased risk of death from infectious causes (standardized mortality ratio [SMR], 4.2; 95% CI, 3.2-5.4), with the greatest risk observed among females (SMR, 3.2; 95% CI, 1.5-6.9) and among those who had been exposed to total body irradiation (SMR, 7.8; 95% CI, 1.8-33.0). Survivors also reported higher rates than siblings of overall infectious complications (RR, 1.3; 95% CI, 1.2-1.4) and higher rates of all categories of infection. CONCLUSIONS: Survivors of childhood cancer remain at elevated risk for developing infectious-related complications, and they have a higher risk of infection-related mortality years after therapy. Further investigation is needed to provide insight into the mechanisms for the observed excess risks.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".