How to make a shared decision with older persons for end-stage kidney disease treatment: the added value of geronto-nephrology
Bibliographic record
Abstract
Improving care for older people with end-stage kidney disease (ESKD) requires the adaptation of standards to meet their needs. This may be complex due to their heterogeneity in terms of multimorbidity, frailty, cognitive decline and healthcare priorities. As benefits and risks are uncertain for these persons, choosing an appropriate treatment is a daily challenge for nephrologists. In this narrative review, we aimed to describe the issues associated with healthcare for older people, with a specific focus on decision-making processes; apply these concepts to the context of ESKD; identify components and modalities of shared decision-making and suggest means to improve care pathways. To this end, we propose a geronto-nephrology dynamic, described here as the necessary collaboration between these specialties. Underscoring gaps in the current evidence in this field led us to suggest priority research orientations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".