An ISN-DOPPS Survey of the Global Impact of COVID-19 Pandemic on Home Peritoneal Dialysis Services
Bibliographic record
Abstract
Background: Home dialysis may be able to minimize SARS-CoV2 exposure risks. The pandemic may have introduced unique challenges related to supply disruption and care delivery changes. We sought to assess the global burden of COVID-19 on peritoneal dialysis units (PD) and understand PD unit practice changes during this time. Methods: The Peritoneal Dialysis/Dialysis Outcomes and Practice Patterns Study (PDOPPS/DOPPS) and International Society of Nephrology (ISN) administered a webbased survey (1) to dialysis units selected based on a random sample stratified by region (November 2020 - March 2021), and (2) to an open invitation via ISN's membership list and social media (March 2021). Responses were compared across 10 ISN regions. Results: Returned surveys included 167 PD facilities across 52 countries. Changes in several care domains including clinic communication and frequency, labwork frequency, method of communication, masking policies, changes in handling of PD effluent among infected individuals, PD supply disruption, access to methods of PD catheter insertion and frequency of new patient training are highlighted (table). Conclusions: Variability exists in routine PD care, and the availability and use of PPE, disruption in PD supplies among the different regions reflecting the availability of the resources and infrastructure differences. LMIC tended to be more severely impacted—this gap needs to be addressed in anticipation of future pandemics for treatment continuity. Although remote technology use among PD patients to communicate with their physicians has increased during the pandemic, optimal communication frequency, methods and schedule of routine bloodwork needs to be better elucidated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".