American College of Rheumatology Guidance for the Management of Rheumatic Disease in Adult Patients During the COVID‐19 Pandemic: Version 3
Bibliographic record
Abstract
OBJECTIVE: To provide guidance to rheumatology providers on the management of adult rheumatic disease in the context of the coronavirus disease 2019 (COVID-19) pandemic. METHODS: A task force, including 10 rheumatologists and 4 infectious disease specialists from North America, was convened. Clinical questions were collated, and an evidence report was rapidly generated and disseminated. Questions and drafted statements were reviewed and assessed using a modified Delphi process. This included asynchronous anonymous voting by email and webinars with the entire panel. Task force members voted on agreement with draft statements using a 1-9-point numerical scoring system, and consensus was determined to be low, moderate, or high based on the dispersion of votes. For approval, median votes were required to meet predefined levels of agreement (median values of 7-9, 4-6, and 1-3 defined as agreement, uncertainty, or disagreement, respectively) with either moderate or high levels of consensus. RESULTS: Draft guidance statements approved by the task force have been combined to form final guidance. CONCLUSION: These guidance statements are provided to promote optimal care during the current pandemic. However, given the low level of available evidence and the rapidly evolving literature, this guidance is presented as a "living document," and future updates are anticipated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.021 |
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".