Bibliographic record
Abstract
People with advanced chronic kidney disease (CKD) have high mortality; substantial physical, emotional and spiritual suffering; and tremendous end-of-life (EOL) care needs. However, their illness trajectories and needs differ from those with advanced cancer and current palliative care models do not meet these needs. Over the last two decades, much research and evidence on advance care planning (ACP) and EOL issues in CKD have accumulated. As a result, integrated renal palliative care services are slowly being developed internationally. Kidney Disease: Improving Global Outcomes (KDIGO), the independent, not-for-profit, organisation that conducts formal international guideline development in CKD, agrees that a comprehensive analysis of ACP and EOL/supportive care for CKD patients is timely and represents an area of great clinical need. KDIGO is therefore partnering with experts from around the world to hold the first consensus forum on renal supportive care. The goal is to (1) summarise the state of knowledge; (2) discuss what recommendations can be derived from the available knowledge; and (3) assess what needs to be undertaken to improve the evidence-base for ACP and EOL clinical management. The overall aim is to work towards global guidelines for the implementation of renal supportive care. This would help improve worldwide practice, referral, and overall access to ACP and palliative care services for patients with CKD. This session will highlight these recent advances in ACP and EOL care and will suggest how new knowledge may be integrated into care for patients with advanced CKD.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".