American College of Rheumatology Guidance for the Management of Pediatric Rheumatic Disease During the COVID‐19 Pandemic: Version 2
Bibliographic record
Abstract
OBJECTIVE: To provide clinical guidance to rheumatology providers who treat children with pediatric rheumatic disease (PRD) in the context of the coronavirus disease 2019 (COVID-19) pandemic. METHODS: The task force, consisting of 7 pediatric rheumatologists, 2 pediatric infectious disease physicians, 1 adult rheumatologist, and 1 pediatric nurse practitioner, was convened on May 21, 2020. Clinical questions and subsequent guidance statements were drafted based on a review of the queries posed by the patients as well as the families and healthcare providers of children with PRD. An evidence report was generated and disseminated to task force members to assist with 3 rounds of asynchronous, anonymous voting by email using a modified Delphi approach. Voting was completed using a 9-point numeric scoring system with predefined levels of agreement (categorized as disagreement, uncertainty, or agreement, with median scores of 1-3, 4-6, and 7-9, respectively) and consensus (categorized as low, moderate, or high). To be approved as a guidance statement, median vote ratings were required to fall into the highest tertile for agreement, with either moderate or high levels of consensus. RESULTS: To date, 39 guidance statements have been approved by the task force. Those with similar recommendations were combined to form a total of 33 final guidance statements, all of which received median vote ratings within the highest tertile of agreement and were associated with either moderate consensus (n = 5) or high consensus (n = 28). CONCLUSION: These guidance statements have been generated based on review of the available literature, indicating that children with PRD do not appear to be at increased risk for susceptibility to SARS-CoV-2 infection. This guidance is presented as a "living document," recognizing that the literature on COVID-19 is rapidly evolving, with future updates anticipated.
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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 | 0.023 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".