American College of Rheumatology Guidance for <scp>COVID</scp>‐19 Vaccination in Patients With Rheumatic and Musculoskeletal Diseases: Version 5
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To provide guidance to rheumatology providers on the use of COVID-19 vaccines for patients with rheumatic and musculoskeletal diseases (RMDs). METHODS: A task force was assembled that included 9 rheumatologists/immunologists, 2 infectious diseases specialists, and 2 public health physicians. After agreeing on scoping questions, an evidence report was created that summarized the published literature and publicly available data regarding COVID-19 vaccine efficacy and safety, as well as literature for other vaccines in RMD patients. Task force members rated their agreement with draft consensus statements on a 9-point numerical scoring system, using a modified Delphi process and the RAND/University of California Los Angeles Appropriateness Method, with refinement and iteration over 2 sessions. Consensus was determined based on the distribution of ratings. RESULTS: Despite a paucity of direct evidence, statements were developed by the task force and agreed upon with consensus to provide guidance for use of the COVID-19 vaccines, including supplemental/booster dosing, in RMD patients and to offer recommendations regarding the use and timing of immunomodulatory therapies around the time of vaccination. CONCLUSION: These guidance statements are intended to provide direction to rheumatology health care providers on how to best use COVID-19 vaccines and to facilitate implementation of vaccination strategies for RMD patients.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it