Possible Consequences of a Shortage of Hydroxychloroquine for Patients with Systemic Lupus Erythematosus amid the COVID-19 Pandemic
Bibliographic record
Abstract
As the coronavirus disease 2019 (COVID-19; the disease caused by SARS-CoV-2) pandemic took hold in North America, rheumatology clinics across the continent were inundated with phone calls from patients with systemic lupus erythematosus (SLE) who were understandably fearful of COVID-19. One of the most common questions from patients was whether they should stop taking their medications. Since the beginning of the epidemic, turned pandemic, our immune-compromised patients with SLE have been overwhelmed with warnings of their higher risk of severe illness1,2. These statements are based on general knowledge of increased infection risk in patients with SLE, extrapolation from other viral illnesses, and expert opinion. However, adding to the confusion, there is no specific information on SLE per se or on any of the commonly used immunosuppressive drugs for SLE3. Even summary statements from those countries farther along the track of this pandemic broadly reference “patients with serious underlying disease” as being at high risk of poor outcomes without particulars4,5. Moreover, some very recent articles focus on the possible benefits of immunosuppressive drugs, both synthetic and biologic, to fight COVID-19, including early rumblings about the potential positive effect of chloroquine and hydroxychloroquine (HCQ)6,7. Then on Thursday, March 19, US President Donald Trump stated that antimalarials showed tremendous promise and “could be a game-changer.” Suddenly, the rumblings became a roar. The questions about stopping HCQ turned into “I can’t get HCQ, my pharmacy is out” from patients with SLE trying to access refills. All over Canada and the United States, news organizations were publishing stories of patients worried about drug supply, pharmacies documenting shortages, hospitals trying to stock up, drug companies promising to ramp up production, governments securing supply to treat patients with COVID-198,9, … Address correspondence to Dr. C.A. Peschken, University of Manitoba, RR149 Arthritis Centre, 800 Sherbrook St., Winnipeg, Manitoba R3A 1M4, Canada. E-mail: christine.peschken{at}umanitoba.ca
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".