SARS-CoV-2 Infection in the Pediatric Oncology Population: The Definitive Comprehensive Report of the Infectious Diseases Working Group of AIEOP
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to assess the clinical impact and outcome of the SARS-CoV-2 infection on children with cancer or those who received a hematopoietic stem cell transplantation. METHODS: AIEOP (Italian Association of Pediatric Hematology and Oncology) performed a nationwide multicenter observational cohort study, including consecutive patients between April 2020 and November 2022. RESULTS: Twenty-five Italian centers participated and 455 patients were enrolled. We reported a significant increasing trend of symptomatic cases over the years, while the number of nonmild infections remained stable. Early infection after oncologic diagnosis (<60 days) and severe neutropenia were identified as independent risk factors for developing moderate, severe, or critical infections. The percentage of patients who were asymptomatic and mildly symptomatic and who stopped chemotherapy reduced over the years of the pandemic. Nine patients died, but no death was attributed to SARS-CoV-2 infection. CONCLUSIONS: SARS-CoV-2 infection presented a self-limiting benign course in the Italian pediatric oncohematology population during the pandemic, and its main consequence has been the discontinuation of cancer-directed therapies. The rate of patients who were asymptomatic and stopped chemotherapy reduced over the years, suggesting that the continuation of chemotherapy is a feasible option.
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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 | 0.001 |
| 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".