Whose Choice Is It? Shared Decision Making in Nephrology Care
Bibliographic record
Abstract
Patients living with end-stage renal disease (ESRD) are faced with numerous decisions across the trajectory of their illness. Shared decision making (SDM) offers a patient-centered approach to engage patients in decision making in meaningful ways. Using an SDM approach, patients and providers collaborate to make healthcare decisions by taking into account the best available empirical evidence, in conjunction with the patient's values, preferences, and individual circumstances. In this article, we outline the principles of SDM; highlight the broad range and context of decisions faced by patients living with ESRD; review decision-support interventions; and consider opportunities and challenges for implementing SDM into usual ESRD practice. A summary of current knowledge and areas for research and further investigation concludes the paper. Because nephrology team members spend a lot of time interacting with patients during treatments and follow-up care, they are well positioned to engage in SDM. Healthcare systems need innovation in communication to ensure the ethical application of important technological improvements in renal treatments, and to ensure that patient decision-support processes are available. SDM is a promising innovation to support the recalibration of care for patients living with end-stage renal disease.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads 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".