Novel anemia therapies in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
Anemia is common in patients with chronic kidney disease and is associated with a high burden of morbidity and adverse clinical outcomes. In 2012, Kidney Disease: Improving Global Outcomes (KDIGO) published a guideline for the diagnosis and management of anemia in chronic kidney disease. Since then, new data from studies assessing established and emerging therapies for the treatment of anemia and iron deficiency have become available. Beginning in 2019, KDIGO planned 2 Controversies Conferences to review the new evidence and its potential impact on the management of anemia in clinical practice. Here, we report on the second of these conferences held virtually in December 2021, which focused on a new class of agents-the hypoxia-inducible factor-prolyl hydroxylase inhibitors (HIF-PHIs). This report provides a review of the consensus points and controversies from this second conference and highlights areas that warrant prioritization for future research.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".