COVD-04. CHARACTERISTICS OF SARS-COV-2 IN 64 CHILDREN WITH CNS TUMORS: A REPORT FROM THE SIOP/ST. JUDE CHILDREN’S RESEARCH HOSPITAL (SJCRH) GLOBAL COVID-19 CHILDHOOD CANCER REGISTRY
Bibliographic record
Abstract
Abstract BACKGROUND The GCCCR is a collaboration between SIOP and SJCRH to describe the natural history of SARS-CoV-2 in children with cancer across the world. METHODS The GCCCR is a deidentified registry of patients <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-days and 60-days post infection. RESULTS As of August 10th 2020, the GCCCR included 730 cases from 35 countries, including 64 children with CNS tumors (8.8%) from 17 countries. The most frequent diagnoses were embryonal tumors (31.2%) and low-grade glioma (17.2%). Thirty-nine (60.9%) children were asymptomatic from infection, while 19 (29.7%) patients required hospital admission and 2 (6.3%) transferred to the intensive care unit. There was a significant association between infection severity and ANC <500 (p=0.04). At the time of infection, 44 (68.8%) patients were undergoing cancer-directed therapy. Thirty-two cases have follow-up data. No modification in cancer-directed therapy occurred in 11 (34.4%) patients, while chemotherapy was modified in 6 (18.8%), radiotherapy delayed in 2 (6.3%), and surgery postponed in 1 (3.1%). No patients died from SARS-CoV-2 infection, although 2 died from non-COVID-19 related causes. CONCLUSION The frequency and severity of COVID infection among children with CNS tumors appears to be proportionally lower compared to other children with cancer. Although this is the largest cohort of patients reported to date, additional insight is needed, including the effects of treatment modifications on outcomes.
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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 | 0.002 | 0.003 |
| 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".