Outcomes of Coronavirus Disease 2019 Infection in Children and Adolescents With Cancer in Canada: Population-based Study and Systematic Review
Bibliographic record
Abstract
Published outcomes for children with cancer with coronavirus disease 2019 (COVID-19) have varied. Outcome data for pediatric oncology patients in Canada, outside of Quebec, have not been reported. This retrospective study captured patient, disease, and COVID-19-related infectious episode characteristics and outcome data for children, 0 to 18 years, diagnosed with a first COVID-19 infection between January 2020 to December 2021 at 12 Canadian pediatric oncology centers. A systematic review of pediatric oncology COVID-19 cases in high-income countries was also undertaken. Eighty-six children were eligible for study inclusion. Thirty-six (41.9%) were hospitalized within 4 weeks of COVID-19; only 10 (11.6%) had hospitalization attributed to the virus, with 8 being for febrile neutropenia. Two patients required intensive care unit admission within 30 days of COVID-19 infection, neither for COVID-19 management. There were no deaths attributed to the virus. Of those scheduled to receive cancer-directed therapy, within 2 weeks of COVID-19, 20 (29.4%) experienced treatment delays. Sixteen studies were included in the systematic review with highly variable outcomes identified. Our findings compared favorably with other high-income country's pediatric oncology studies. No serious outcomes, intensive care unit admissions, or deaths, in our cohort, were directly attributable to COVID-19. These findings support the minimization of chemotherapy interruption after COVID-19 infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".