Challenges for sustainable end-stage kidney disease care in low-middle-income countries: the problem of the workforce
Bibliographic record
Abstract
Prevention and early detection of kidney diseases in adults and children should be a priority for any government health department. This is particularly pertinent in the low-middle-income countries, mostly in Asia, Africa, Latin America, and the Caribbean, where up to 7 million people die because of lack of end-stage kidney disease treatment. The nephrology workforce (nurses, technicians, and doctors) is limited in these countries and expanding the size and expertise of the workforce is essential to permit expansion of treatment for both chronic kidney disease and end-stage kidney disease. To achieve this will require sustained action and commitment from governments, academic medical centers, local nephrology societies, and the international nephrology community.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".