A global snapshot on health systems capacity for detection, monitoring, and management of acute kidney injury: A multinational study from the ISN-GKHA
Bibliographic record
Abstract
Acute kidney injury (AKI) is a significant cause of morbidity and mortality, especially in low and lower-middle income countries. Data from the third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) were used to evaluate the organization of structures and services for the provision of AKI care in world countries and ISN regions. An international survey of key stakeholders (clinicians, policymakers, and patient advocates) from countries affiliated with the ISN was conducted from July to September 2022 to assess structures and services for AKI care across countries. Main findings of the study show that overall, 167 countries or jurisdictions participated in the survey, representing 97.4% of the world's population. Only 4% of countries had an AKI detection program based on national policy or guideline, and 50% of these countries used a reactive approach for AKI identification (i.e., cases managed as identified through clinical practice). Only 19% of national governments recognized AKI as a healthcare priority. Almost all countries (98% of the countries surveyed) reported capacity to provide acute hemodialysis (HD) for AKI, but in 31% of countries, peritoneal dialysis (PD) was unavailable for AKI. About half of all countries (44% of countries surveyed) provided acute dialysis (HD or PD) via public funding, but funding availability varied across ISN regions, including less than a quarter of countries in Oceania and South East Asia (17%) and Africa (24%) and highest availability in Western Europe (91%). Availability increased with the increasing country income level. Initiatives have been developed to propose and promote optimal care for AKI (including the ISN 0-by-25 initiative), but capacity for optimal AKI care remains low, particularly in low- and lower-middle-income countries. Concerted efforts by the global community are required to close these gaps, to improve AKI outcomes across the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".