Bibliographic record
Abstract
Chronic kidney disease (CKD) is an increasingly important global public health issue that affects more than 10% of the worldwide population [1] The incidence and prevalence of CKD has increased exponentially worldwide in recent decades and it is now estimated that 850 million people are living with CKD around the globe. The World Health Organization ranked CKD as the 10th leading cause of death in 2020, and it is forecast to become the 5th leading cause of death by 2040 [2]. Kidney replacement therapy (KRT) is a life-sustaining treatment for patients with kidney failure, of which there are estimated to be between 5 and 7 million worldwide [3]. While the CKD epidemic is a global issue, the burden of this disease falls disproportionately on low-income countries (LICs) and lower-middle-income countries (LMICs). It is expected that by 2030, >70% of people with kidney failure will live in LICs, where <10% of patients with kidney failure are able to access KRT [4]. Therefore, it is important to have reliable data on the current status of kidney care services across countries, which can be used to guide policies and strategies to improve care in LICs and LMICs. The International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) is a multinational survey that collects information on the current capacity for kidney care across all world regions. The previous iterations of the survey reported in 2017 [5] and 2019 [6] identified low recognition of CKD as a health priority as well as important gaps in the availability, accessibility and affordability of KRT between countries. This supplement of Nephrology Dialysis Transplantation (NDT) presents results from the third iteration of the ISN-GKHA [7], which has expanded to include additional countries. In this supplement… Yeung et al. [8] evaluate funding models for provision of KRT, services for management of CKD and reimbursement of medications. Oversight structures and delivery of kidney care are also considered. Htay et al. [9] and Cho et al. [10] assess the availability, accessibility, affordability and quality of hemodialysis and peritoneal dialysis, respectively, across ISN regions and World Bank income groups. Viecelli et al. [11] present an update on the global incidence and prevalence of kidney transplantation, as well as the availability, accessibility, affordability and quality of kidney transplantation. Hole et al. [12] provide a more detailed examination of non-dialytic management of kidney failure (conservative kidney management) than in previous iterations of the ISN-GKHA, considering the global availability, infrastructure, guidelines, medications and training. Okpechi et al. [13] discuss the global kidney care workforce, including the availability of nephrologists and nephrology trainees, and shortages in the workforce required for optimal delivery of kidney care. Finally, Irish et al. [14] assess the global capacity for data monitoring and surveillance which are essential for governance, regulation, planning and policy development for chronic disease care. We hope that the readers of NDT appreciate this article collection summarizing key findings from the most recent iteration of the ISN-GKHA, which highlights the persisting disparities in kidney care services across countries. The Clinical Trial Service Unit and Epidemiological Studies Unit (Oxford, UK) has a staff policy of not accepting honoraria or other payments from the pharmaceutical industry, except for the reimbursement of costs to participate in scientific meetings (see https://www.ctsu.ox.ac.uk/about/ctsu_honoraria_25june14-1.pdf). N.S. reports grant funding paid to their institution (the University of Oxford) from Boehringer Ingelheim, Eli Lilly and Novo Nordick, and funding from the United Kingdom Medical Research Council (MRC) (to the Clinical Trial Service Unit and Epidemiological Studies Unit; reference no. MC_UU_00017/3), the British Heart Foundation, National Institute for Health and Care Research Biomedical Research Council, and Health Data Research (UK). This supplement was supported by the International Society of Nephrology (Grant RES0033080 to the University of Alberta). The International Society of Nephrology provided administrative support for the design and implementation of the survey and data collection activities and the Alberta Kidney Disease Network staff aided with data analysis.
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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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.043 | 0.048 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".