The Global Impact of the COVID-19 Pandemic on In-Center Hemodialysis Services: An ISN-Dialysis Outcomes Practice Patterns Study Survey
Bibliographic record
Abstract
INTRODUCTION: To assess the impact of the COVID-19 pandemic impact on hemodialysis (HD) centers, The Dialysis Outcomes and Practice Patterns Study and ISN collaborated on a web-survey of centers. METHODS: A combined approach of random sampling and open invitation was used between March 2020 and March 2021. Responses were obtained from 412 centers in 78 countries and all 10 ISN regions. RESULTS: In 8 regions, rates of SARS-CoV-2 infection were <20% in most centers, but in North East Asia (NE Asia) and Newly Independent States and Russia (NIS & Russia), rates were ≥20% and ≥30%, respectively. Mortality was ≥10% in most centers in 8 regions, although lower in North America and Caribbean (N America & Caribbean) and NE Asia. Diagnostic testing was not available in 33%, 37%, and 61% of centers in Latin America, Africa, and East and Central Europe, respectively. Surgical masks were widely available, but severe shortages of particulate-air filter masks were reported in Latin America (18%) and Africa (30%). Rates of infection in staff ranged from 0% in 90% of centers in NE Asia to ≥50% in 63% of centers in the Middle East and 68% of centers in NIS & Russia. In most centers, <10% of staff died, but in Africa and South Asia (S Asia), 2% and 6% of centers reported ≥50% mortality, respectively. CONCLUSION: There has been wide global variation in SARS-CoV-2 infection rates among HD patients and staff, personal protective equipment (PPE) availability, and testing, and the ways in which services have been redesigned in response to the pandemic.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".