An ISN-DOPPS Survey of the Global Impact of the COVID-19 Pandemic on In-Centre Haemodialysis Services
Bibliographic record
Abstract
Background: Haemodialysis units (HDUs) have had to rapidly adapt practices and policies to safely continue life-sustaining HD services during the COVID-19 pandemic. We aimed to describe the impact of COVID-19 in different parts of the world. Methods: The Dialysis Outcomes and Practice Patterns Study (DOPPS) and International Society of Nephrology (ISN) collaborated to web-survey individual HDUs. Responses were obtained in three ways: (1) a survey of DOPPS sites in China (May/ June 2020), (2) a random sample (20 units if > 40 units/ country; all units if < 40) stratified by region and HDU census (November 2020 - March 2021), and (3) an open invitation via ISN's membership list and social media (March 2021). Responses were compared between the ten ISN regions. Results: There were returns from 412 HDUs (46% public sector, 79% urban; 70% adult, 2% paediatric, 28% adult & paediatric) from 78 countries (9% low-, 24% lower-middle-, 28% upper-middle-, 39% high-income). Conclusions: The COVID-19 pandemic has had a significant impact on dialysis services and staffing worldwide. Differences in uptake of policies and practices across regions have likely been because of variable access to resources to enable implementation of diagnostic testing algorithms and adequate supply of PPE to implement infection prevention and control recommendations. Guidance should be consistent, adaptable to (nearly) all situations and locations, and evidence based. Going forward, the operationalisation of vaccine programs should be incorporated into guidelines. Disruptions to dialysis services should be minimised, and resource provision (including vaccines) prioritised by policymakers and governments in future waves of COVID-19 and pandemics if we are to protect HD patients, staff, and services.Dialysis facility COVID-19 related resources, practices, and outcomes, as reported unit manager at each participating site
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".