Authors’ Reply: “The Advancing American Kidney Health Initiative: The Challenge of Overcoming the Status Quo” and “The Advancing American Kidney Health Initiative: Do Not Let 80% Distract Us from the Fact that We Can Do Better”
Bibliographic record
Abstract
On behalf of the authors, I would like to thank Fissel et al.1 and Weiner et al.2 for their letters regarding our recent publication in JASN.3 We agree that the 2019 US Presidential Executive Order on Advancing American Kidney Health is important and represents strong endorsement of the need to improve our approach to the prevention and treatment of CKD. Unfortunately, the authors of both letters seem to have misinterpreted the point of our paper and we would like to take this opportunity to clarify our message. We are proponents of shared decision making and offering all treatment options to appropriate candidates, including conservative care. However, we were in no way suggesting that the path to a higher percentage of patients treated with home dialysis or kidney transplantation was by forcing patients to choose conservative kidney management. On the contrary, we were concerned that the performance measures proposed might have the unintended consequence of reducing patient choice and encouraging providers to steer individuals who are not transplant or home dialysis candidates toward conservative care. As stated in the paper, the only way to hit the target of 80% in a reasonable time frame is to reduce the numbers of patients treated with dialysis—effectively reducing the denominator. To be clear, we were not encouraging this, we were instead concerned that it might be the only realistic way to hit the targets put forward and that realization may incent providers to restrict patient choice and discourage shared decision making, particularly if the consequences of not hitting targets are severe enough. There is no doubt that investing in innovation and finding new ways to tackle the problems that patients and providers currently face is extremely important. We share Fissel et al.’s1 enthusiasm for any innovation that could expand the numerator, but we are aware of no such innovation that has been shown to expand the pool of patients on home dialysis to the degree required to hit an 80% target by 2025. Contrary to the authors’ assertion that our issues with targets are borne out of a lack of imagination or complacency regarding the status quo, they are instead motivated by a pragmatic view of the current reality and need to address the performance measures that are being proposed today that could negatively affect patients if they are not carefully considered. As physicians, researchers, and advocates for patients with kidney disease, we very much hope innovation transforms the way we think about the prevention and treatment of kidney disease in the near future. Disclosures P.G. Blake reports serving in an advisory or leadership role on the editorial board of the American Journal of Nephrology, and as medical director of Ontario Renal Network (this is a paid role); and receiving honoraria from Baxter Global. M.J. Oliver reports receiving honoraria from Amgen, Baxter Healthcare, and Janssen; having patents or royalties via Oliver Medical Management Inc. as co-owner of a Canadian patent for DMAR systems; having other interests in or relationships with contracted medical leads at Ontario Health and Ontario Renal Network; being the sole owner of Oliver Medical Management Inc., which is a private corporation that licenses the Dialysis Measurement Analysis and Reporting (DMAR) software system; and being employed by MJ Oliver Medical Professional Corporation. R.R. Quinn reports having consultancy agreements with Baxter Corporation; receiving honoraria from Baxter (PD University) and Baxter Advisory Board; receiving research funding from the International Society for Peritoneal Dialysis PD Catheter Registry (Baxter partially funded project); and having a Canadian patent for a DMAR System. M. Tonelli reports serving in an advisory or leadership role for the American Journal of Kidney Disease, Kidney Disease, Kidney Disease Improving Global Outcomes, and Kidney International; receiving honoraria from AstraZeneca (lecture fees); and having other interests in or relationships with Canadian Institutes of Health Research and Gilead (for expert testimony). All remaining authors have nothing to disclose. Funding None.
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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 | 0.008 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.047 | 0.060 |
| Insufficient payload (model declined to judge) | 0.018 | 0.019 |
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".