Perspectives of Patients and Care Partners on Prognostic Discussions in CKD: A Qualitative Study
Bibliographic record
Abstract
Background: The incidence and prevalence of chronic kidney disease (CKD) is expected to rise over the next decade. Adults with severe CKD must navigate difficult decisions related to kidney replacement therapy and the competing risk of death without kidney failure. In this qualitative study, we sought to explore the experiences of patients with CKD and their care partners related to discussions about kidney failure risks and mortality, and to elicit their preferences for discussing and using prognostic information. Methods: We purposively sampled patients with non-dialysis-dependent CKD (eGFR <30 mL/min/1.73m2) followed in multi-disciplinary CKD clinics and their care partners in Alberta, Canada. We conducted online or telephone-based, semi-structured interviews that centered around eliciting personal experiences and responses to clinical vignettes that included risk estimates for kidney failure and mortality. Interviews were audio recorded and transcribed verbatim. Data were coded iteratively and in duplicate and analyzed through reflexive thematic analysis. Results: We conducted interviews with 22 patients and 7 care partners. Participants emphasized the quality of communication and interactions with their healthcare team as the greatest influences on their prognostic understanding. Participants’ experiences and preferences related to prognostic discussions are elaborated across the following themes: 1) Alignment of discussion context with informational readiness (appropriate timing, setting, roles), 2) Level of directness in conveying individual risk (sensitivity, frankness, professional obligation), 3) Quality of personal and therapeutic relationships impacting preferences (reliance on trust and support structures), and 4) Perceived value and applicability of prognostic information (personalized information, utility in care planning). Conclusion: The perceived quality of prognostic discussions among patients with CKD and their care partners was influenced by contextual factors, individual preferences, and communication styles. Strategies tailored to individuals’ readiness and the collaborative CKD care setting may improve understanding of clinical outcomes of severe CKD and use of prognostic information in treatment planning.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".