Severe infections following treatment for childhood cancer: a report from CYP-C
Bibliographic record
Abstract
Little is known about infections occurring after childhood cancer treatment. We assessed the risk of severe infection postcancer therapy in survivors of leukemia compared to other cancer types. We performed a population-based cohort study of children <15 years of age diagnosed with cancer (2001-2016), alive and relapse-free 30 days after treatment completion. The risk of severe infection in both groups was estimated using subdistribution proportional hazard regression. We identified 6148 survivors (1960 with leukemia). The cumulative incidence (95% confidence interval) of severe infections at 3 years was 0.70% (0.40-1.2%) in leukemia and 0.51% (0.32-0.79%) in other cancers. The risk of severe infection was not statistically different in leukemia survivors compared to other cancer types in univariate and multivariate analysis (adjusted hazard ratio: 1.40, 95% CI: 0.69-2.85). No significant association was found between a history of leukemia and an increased risk of severe infection after treatment, compared to other cancer types.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".