Understanding kidney care needs and implementation strategies in low- and middle-income countries: conclusions from a “Kidney Disease: Improving Global Outcomes” (KDIGO) Controversies Conference
Bibliographic record
Abstract
Evidence-based cinical practice guidelines improve delivery of uniform care to patients with and at risk of developing kidney disease, thereby reducing disease burden and improving outcomes. These guidelines are not well-integrated into care delivery systems in most low- and middle-income countries (LMICs). The KDIGO Controversies Conference on Implementation Strategies in LMIC reviewed the current state of knowledge in order to define a road map to improve the implementation of guideline-based kidney care in LMICs. An international group of multidisciplinary experts in nephrology, epidemiology, health economics, implementation science, health systems, policy, and research identified key issues related to guideline implementation. The issues examined included the current kidney disease burden in the context of health systems in LMIC, arguments for developing policies to implement guideline-based care, innovations to improve kidney care, and the process of guideline adaptation to suit local needs. This executive summary serves as a resource to guide future work, including a pathway for adapting existing guidelines in different geographical regions.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".