Outcomes of SARS‐CoV‐2 infection in 126 children and adolescents with central nervous system tumors
Bibliographic record
Abstract
BACKGROUND: The Global Registry of COVID-19 in Childhood Cancer (GRCCC) seeks to describe the natural history of SARS-CoV-2 in children with cancer across the world. Here, we report the disease course and management of coronavirus disease 2019 (COVID-19) infection in the subset of children and adolescents with central nervous system (CNS) tumors who were included in the GRCCC until February 2021, the first data freeze. PROCEDURE: The GRCCC is a deidentified web-based registry of patients less than 19 years of age with cancer or recipients of a hematopoietic stem cell transplant and laboratory-confirmed SARS-CoV-2 infection. Demographic data, cancer diagnosis, cancer-directed therapy, and clinical characteristics of SARS-CoV-2 infection were collected. Outcomes were collected at 30 and 60 days post infection. RESULTS: The GRCCC included 1500 cases from 45 countries, including 126 children with CNS tumors (8.4%). Sixty percent of the cases were from middle-income countries, while no cases were reported from low-income countries. Low-grade gliomas, high-grade gliomas, and CNS embryonal tumors were the most common CNS cancer diagnoses (67%, 84/126). Follow-up at 30 days was available for 107 (85%) patients. Based on the composite measure of severity, 53.3% (57/107) of reported SARS-CoV-2 infections were asymptomatic, 39.3% (42/107) were mild/moderate, and 6.5% (7/107) were severe or critical. One patient died from SARS-CoV-2 infection. There was a significant association between infection severity and absolute neutrophil count less than 500 (p = .04). Of 107 patients with follow-up available, 40 patients (37.4%) were not receiving cancer-directed therapy. Thirty-four patients (50.7%) had a modification to their treatment due to withholding of chemotherapy or delays in radiotherapy or surgery. CONCLUSION: In this cohort of patients with CNS tumors and COVID-19, the frequency of severe infection appears to be low, although severe disease and death do occur. We found that greater severity was seen in patients with severe neutropenia, although treatment modifications were not associated with infection severity or cytopenias. Additional analyses are needed to further describe this unique group of patients.
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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 | 0.001 |
| 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 | 0.001 |
| 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".