The Future of COVID-19 for Patients With Immune-Mediated Inflammatory Diseases: Who Is at Risk?
Bibliographic record
Abstract
For the rheumatologist, one of the greatest challenges since the start of the coronavirus disease 2019 (COVID-19) pandemic has been determining which of our patients are at greatest risk for severe COVID-19, and who should be triaged for aggressive outpatient management with preexposure prophylaxis (PrEP), additional vaccine doses, and treatment with antivirals and/or monoclonal antibody products when infected. At present, although the US federal government declared an end to the public health emergency for COVID-19 in May 2023, SARS-CoV-2 is still with us. The pandemic has now morphed into 2 epidemics: one of immunocompetent vaccinated persons, and one of the immunocompromised. The question remains: Which immunocompromised patients are at greatest risk? Confusion surrounding who is “high risk” and an optimal candidate for PrEP products, vaccine boosters, and antiviral treatments stems largely from lumping “immunocompromised persons” into one bucket. Three percent of the US population is classified as being immunocompromised, yet we are well aware that this is a highly heterogeneous group, encompassing a highly diverse range of immunosuppressed states that range from well-controlled patients with HIV with normal or near-normal immune function, to highly vulnerable bone marrow transplant recipients. Even within the realm of rheumatologic patients, there is a high degree of diversity with regard to their immunosuppressive state and vulnerability to serious infectious complications based on variables such as age, comorbidities, and especially immunosuppressive regimens. Even within the category of biologic and targeted therapies, there is marked heterogeneity, as a 50-year-old patient on monotherapy with an interleukin (IL)-23 inhibitor would be viewed by most clinicians to be in a different COVID-19 risk category than a 40-year-old on a B cell–depleting drug. Centers for Disease Control and Prevention (CDC) guidance on who are the moderately-to-severely immunocompromised is far from clear, particularly in regard to the effects of immunotherapeutics.1 This … Address correspondence to Dr. C. Calabrese, 9500 Euclid Ave, Desk A50, Cleveland, OH 44106, USA. Email: calabrc{at}ccf.org.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".