COVID-19 Update for the GRAPPA 2021 Annual Meeting: Focus on COVID-19 Vaccination
Bibliographic record
Abstract
The efficacy and safety of coronavirus disease 2019 (COVID-19) vaccination in patients with autoimmune inflammatory diseases (AIRDs) who are treated with immunomodulatory therapies was the focus of a symposium at the 2021 virtual annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA). The keynote address was delivered by Dr. Jeffrey Curtis, chair of the American College of Rheumatology COVID-19 Vaccine Clinical Guidance task force, detailing what we do and do not know about vaccine efficacy and safety in patients with AIRDs and providing guidance about the need for modification of dosing in some immunomodulatory medications for optimal vaccine response. A consensus of the task force was that all patients with AIRDs should be vaccinated as soon as it is allowed in their respective locations, since the benefits of increased protection against COVID-19 infection outweigh the potential for vaccination reactions, including flares of underlying disease, or for reduced efficacy of vaccination because of disease state or medications. Key issues among patient research partners with psoriatic disease expressed in the premeeting survey and panel discussion/question-and-answer period included: vaccine efficacy and safety, the need to continue safe social habits and masking, how to assess efficacy of vaccination, how to deal with vaccine hesitancy among social contacts, medication management relative to vaccination, and concerns about the adequacy of ongoing telehealth visits vs the convenience of that technology.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 0.022 |
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".