Kidney health for all: preparedness for the unexpected in supporting the vulnerable
Bibliographic record
Abstract
As the rate of natural disasters and other devastating events caused by human activities increases, the burden on the health and well-being of those affected by kidney disease has been immeasurable. Health system preparedness, which involves creating a resilient system that is able to deal with the health needs of the entire community during times of unexpected disruptions to usual care, has become globally important. In the wake of the COVID-19 pandemic, there is a heightened awareness of the amplification of negative effects on the renal community. Paradoxically, the complex medical needs of those who have kidney diseases are not met by systems handling crises, often compounded by an acute increase in burden via new patients as a result of the crisis itself. Disruptions in kidney care as a result of unexpected events are becoming more prevalent and likely to increase in the years to come. It is therefore only appropriate that the theme for this year's World Kidney Day will focus on Kidney Health for All: preparedness for the unexpected in supporting the vulnerable.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.016 | 0.039 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".