Incidence and impact of prior history of serious infection in paediatric lymphoma: A population‐based study
Bibliographic record
Abstract
Children, adolescents and young adults (CAYA) with lymphoma may have undiagnosed inborn errors of immunity (IEI). We assessed the prevalence of prior severe infections in CAYA lymphoma and evaluated its association with post-lymphoma outcomes through a population-based cohort of Ontario CAYA aged 0-21 years diagnosed with Hodgkin or non-Hodgkin lymphoma from 1992 to 2022, matching each case to five controls. Population-based healthcare data identified pre-diagnosis infection-related encounters. We also compared post-diagnosis intensive care unit (ICU) admissions and mortality in lymphoma patients with and without pre-lymphoma infection-related ICU admissions. 2950 CAYA (mean age diagnosis 15.5 ± 4.8 years) and 14 750 matched controls were included. Infection-related ICU admissions were nearly nine times more common among cases versus controls [4.8% vs. 0.6%; odds ratio [OR] 8.9 (95% confidence interval [95% CI]: 6.7-11.7); p < 0.0001]. CAYA with lymphoma and pre-lymphoma infection-related ICU admissions had significantly higher risks of post-lymphoma ICU admissions (6-month incidence: 38.5% vs. 6.6%; hazard ratio [HR] 7.3 [95% CI: 5.7-9.3]; p < 0.0001) and mortality (5-year overall survival: 66.6% vs. 93.5%; HR 6.6 [95% CI: 5.0-8.6]; p < 0.0001) than those without such a history. Findings did not differ by lymphoma subtype or age at diagnosis. A significant subset of CAYA with lymphoma likely has an undiagnosed IEI, with higher post-lymphoma infection and mortality risks. Systematic IEI evaluations may be warranted.
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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 | 0.000 | 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".