Controversies in optimal anemia management: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Conference
Bibliographic record
Abstract
In chronic kidney disease, anemia and disordered iron homeostasis are prevalent and associated with significant adverse consequences. In 2012, Kidney Disease: Improving Global Outcomes (KDIGO) issued an anemia guideline for managing the diagnosis, evaluation, and treatment of anemia in chronic kidney disease. Since then, new data have accrued from basic research, epidemiological studies, and randomized trials that warrant a re-examination of previous recommendations. Therefore, in 2019, KDIGO decided to convene 2 Controversies Conferences to review the latest evidence, explore new and ongoing controversies, assess change implications for the current KDIGO anemia guideline, and propose a research agenda. The first conference, described here, focused mainly on iron-related issues, including the contribution of disordered iron homeostasis to the anemia of chronic kidney disease, diagnostic challenges, available and emerging iron therapies, treatment targets, and patient outcomes. The second conference will discuss issues more specifically related to erythropoiesis-stimulating agents, including epoetins, and hypoxia-inducible factor-prolyl hydroxylase inhibitors. Here we provide a concise overview of the consensus points and controversies resulting from the first conference and prioritize key questions that need to be answered by future research.
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 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 | 0.094 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.023 | 0.043 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".