Worldwide Early Impact of COVID-19 on Dialysis Patients and Staff and Lessons Learned: A DOPPS Roundtable Discussion
Bibliographic record
Abstract
As the worst global pandemic of the past century, coronavirus disease 2019 (COVID-19) has had a disproportionate effect on maintenance dialysis patients and their health care providers. At a virtual roundtable on June 12, 2020, Dialysis Outcomes and Practice Patterns Study (DOPPS) investigators from 15 countries in Asia, Europe, and the Americas described and compared the effects of COVID-19 on dialysis care, with recent updates added. Most striking is the huge difference in risk to dialysis patients and staff across the world. Per-population cases and deaths among dialysis patients vary more than 100-fold across participating countries, mirroring burden in the general population. International data indicate that the case-fatality ratio remains at 10% to 30% among dialysis patients, confirming the gravity of infection, and that cases are much more common among in-center than home dialysis patients. This latter finding merits urgent study because in-center patients often have greater community exposure, and in-center transmission may be uncommon under optimal protocols. Greater telemedicine use is a welcome change here to stay, and our community needs to improve emergency planning and protect dialysis staff from the next pandemic. Finally, the pandemic's challenges have prompted widespread partnering and innovation in kidney care and research that must be sustained after this global health crisis.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".