Answers to common questions about COVID‐19 vaccines in children with cancer
Bibliographic record
Abstract
BACKGROUND: The SARS-CoV-2 outbreak in 2020 evolved into a global pandemic, and COVID-19 vaccines became rapidly available, including for pediatric patients. However, questions emerged that challenged vaccine acceptance and use. We aimed to answer these questions and give recommendations applicable for use in pediatric patients with cancer by healthcare professionals and the public. METHODS: A 12-member global COVID-19 Vaccine in Pediatric Oncology Working Group made up of physicians and nurses from all world regions met weekly from March to July 2021. We used a modified Delphi method to select the top questions. The Working Group, in four-member subgroups, answered assigned questions by providing brief recommendations, followed by a discussion of the rationale for each answer. All Working Group members voted on each recommendation using a scale of 1 to 10, 10 being complete agreement. A "pass" recommendation corresponded to an agreement ≥7.5. RESULTS: We selected 15 questions from 173 suggested questions. Based on existing published information, we generated answers for each question as recommendations. The overall average agreement for the 24 recommendations was 9.5 (95% CI 9.4-9.6). CONCLUSION: Top COVID-19 vaccine-related questions could be answered using available information. Reports on COVID-19 vaccination and related topics have been published at record speed, aided by available technology and the priority imposed by the pandemic; however, all efforts were made to incorporate emerging information throughout our project. Recommendations will be periodically updated on a dedicated website.
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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 | 0.061 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".