Methodological considerations for end-of-life research in patients with chronic kidney disease
Bibliographic record
Abstract
Abstract Good quality information is lacking about how patients die. This is particularly true for chronic kidney disease (CKD) patients who have extensive and unique end-oflife care considerations and needs. Research specific to CKD is required to develop systematic and comprehensive information on the end-of-life care needs and the quality of end-of-life care for patients with CKD, to describe the effectiveness of existing end-of-life care strategies in CKD (including cost-effectiveness), to identify areas that should be priorities for improvement and ultimately to develop innovative strategies for improving the end-of-life care for patients dying with CKD. This paper will highlight the challenges and limitations of the current approaches to end-of-life care research and will outline what is required, both in content and methodology, to move the field of renal palliative care forward.
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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 | 0.759 | 0.835 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".