A Population-Based Study of COVID-19 Infection Among Childhood Cancer Survivors
Bibliographic record
Abstract
Childhood cancer survivors are known to be at risk of chronic co-morbidities, although their risk of COVID-19 infection remains uncertain. Understanding the risk of COVID-19 in this population is necessary to counsel survivors and inform potential mitigation strategies. The objective of this study was to determine whether the rates of COVID-19 infection differed between childhood cancer survivors and the general population. Administrative health care data from a population-based registry of children and adolescents diagnosed with cancer in Ontario, Canada, were linked with a universal health insurance registry and a repository of laboratory data. Rates of COVID-19 testing, test positivity and infection between March 1, 2020 and March 31, 2021 among childhood cancer survivors (n = 10 242) were compared to matched controls from the general population (n = 49 068). Compared to the general population, childhood cancer survivors were more likely to have COVID-19 testing (35.9% [95% CI, 34.5–37.4%] vs. 32.0% [95% CI, 31.4–32.6%]), but had a lower likelihood of positive COVID-19 result among those tested (4.3% [95% CI, 3.6–4.9%] vs. 5.5% [95% CI, 5.1–5.8%]) and a similar rate of infection among all subjects at risk (1.5% [95% CI, 1.3–1.8%] vs. 1.7% [95% CI, 1.6–1.9%]). These findings can inform counseling of survivors and clinician recommendations for this population.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".