Canadian Society of Nephrology COVID-19 Rapid Response Team Home Dialysis Recommendations
Bibliographic record
Abstract
PURPOSE OF PROGRAM: This paper will provide guidance on how to best manage patients with end-stage kidney disease who will be or are being treated with home dialysis during the COVID-19 pandemic. SOURCES OF INFORMATION: Program-specific documents, pre-existing, and related to COVID-19; documents from national and international kidney agencies; national and international webinars, including webinars that we hosted for input and feedback; with additional information from formal and informal review of published academic literature. METHODS: Members of the Canadian Society of Nephrology (CSN) Board of Directors solicited a team of clinicians and administrators with expertise in home dialysis. Specific COVID-19-related themes in home dialysis were determined by the Canadian senior renal leaders community of practice, a group compromising medical and administrative leaders of provincial and health authority renal programs. We then developed consensus-based recommendations virtually by the CSN work-group with input from ethicists with nephrology training. The recommendations were further reviewed by community nephrologists and over a CSN-sponsored webinar, attended by 225 kidney health care professionals, for further peer input. The final consensus recommendations also incorporated review by the editors at the Canadian Journal of Kidney Health and Disease (CJKHD). KEY FINDINGS: We identified 7 broad areas of home dialysis practice management that may be affected by the COVID-19 pandemic: (1) peritoneal dialysis catheter placement, (2) home dialysis training, (3) home dialysis management, (4) personal protective equipment, (5) product delivery, (6) minimizing direct health care provider and patient contact, and (7) assisted peritoneal dialysis in the community. We make specific suggestions and recommendations for each of these areas. LIMITATIONS: This suggestions and recommendations in this paper are expert opinion, and subject to the biases associated with this level of evidence. To expedite the publication of this work, a parallel review process was created that may not be as robust as standard arms' length peer-review processes. IMPLICATIONS: These recommendations are intended to provide the best care possible during a time of altered priorities and reduced resources.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.016 |
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".