The COVID-19 Pandemic and Rheumatology: Impact on Providing Care in Latin America and Around the World
Bibliographic record
Abstract
The novel coronavirus pandemic has affected the world, importantly, from a health perspective. Initial concern about all rheumatology patients being at risk has given way to a more nuanced view of the risks.1 The risk factors for poor outcomes are the same as in nonrheumatological patients, except some medications such as rituximab also increase risk.2 There are also potentially regional differences, as has been reported for Latin America.3 Now that vaccines are becoming available, we can start to see how a path back to “normal” might be achieved. In the midst of all of this, however, are the regions still battling massive infection rates, such as India and South America. Globally, healthcare professionals and healthcare systems have been forced to rapidly adapt and respond to the demands of care. Physicians have been redeployed to the front lines of the coronavirus disease 2019 (COVID-19) pandemic, and healthcare systems have been redesigned to care for the rising numbers of patients. As a response to the surge of cases, and this is particularly true during the first waves of the pandemic, the COVID-19 focus of healthcare systems worldwide led to neglect of other ongoing needs of patients.4 In this issue of The Journal of Rheumatology , Fernández-Ávila and colleagues present the results of an observational cross-sectional survey of rheumatologists in Latin America from non-English speaking countries who were affiliated with the Pan American League of Associations for Rheumatology (PANLAR).5 Their goal was to describe attitudes and practices among rheumatologists during the pandemic. The survey was conducted over 30 days during June and July 2020. At that point, South America was on an upward curve of infection, which we now know would go on to cause devastating rates … Address correspondence to Assoc. Prof. P.C. Robinson, University of Queensland School of Clinical Medicine, Royal Brisbane & Women’s Hospital, Herston, Queensland 4006, Australia. Email: philip.robinson{at}uq.edu.au.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".