Dialysis Care around the World: A Global Perspectives Series
Bibliographic record
Abstract
Worldwide, ESKD prevalence per million population (PMP) has steadily increased from 2003 to 2016 (1), with the greatest proportional increases occurring in lower- and middle-income countries (2). Although dialysis is a lifesaving therapy, it is also extraordinarily expensive, so its use is limited in lower-income countries with less resources available for healthcare. Specifically, the prevalence of dialysis in 2010 was 1176 PMP in higher-income countries, 688 PMP in upper-middle-income countries, 170 PMP in lower-income countries, and 16 PMP in lower-income countries (2). The most common modality of kidney replacement therapy globally is dialysis (78%) and, among patients receiving dialysis, only 11% receive peritoneal dialysis (3). The Kidney360 Global Dialysis Perspective series launched in 2020 and showcases how dialysis is practiced, delivered, and financed in different countries across the world. To date, we have featured perspectives from 17 countries in six continents: Africa (Senegal, South Africa), Asia (India, Israel, Japan, Korea, Singapore, Thailand, Vietnam), Australia, Europe (Spain), North America (Canada, Mexico, United States), and South America (Argentina, Brazil, Guatemala) (4⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓ ⇓–20). Authors of each global perspective were asked to report standard information about their dialysis populations, including general characteristics of the dialysis system and its treatments, such as percentage of patients by dialysis modality, dialysis-unit financing (for profit versus nonprofit), reimbursement (public or private insurance, or self-pay), unit location (hospital versus freestanding), staffing (proportion of nurses versus patient-care technicians and nurse/patient ratios), hemodialysis frequency and session length, and frequency of nephrologist visits. Authors also discussed key challenges and needs unique to their countries, with many discussing potential strategies to improve care moving forward. These perspectives provide fascinating insights about dialysis care in individual countries. Although the availability of dialysis correlates roughly with a country's …
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".