Nephrologists’ Perspectives on Defining and Applying Patient-Centered Outcomes in Hemodialysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patient centeredness is widely advocated as a cornerstone of health care, but it is yet to be fully realized, including in nephrology. Our study aims to describe nephrologists' perspectives on defining and implementing patient-centered outcomes in hemodialysis. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Face-to-face, semistructured interviews were conducted with 58 nephrologists from 27 dialysis units across nine countries, including the United States, the United Kingdom, Australia, Austria, Belgium, Canada, Germany, Singapore, and New Zealand. Transcripts were thematically analyzed. RESULTS: We identified five themes on defining and implementing patient-centered outcomes in hemodialysis: explicitly prioritized by patients (articulated preferences and goals, ascertaining treatment burden, defining hemodialysis success, distinguishing a physician-patient dichotomy, and supporting shared decision making), optimizing wellbeing (respecting patient choice, focusing on symptomology, perceptible and tangible, and judging relevance and consequence), comprehending extensive heterogeneity of clinical and quality of life outcomes (distilling diverse priorities, highly individualized, attempting to specify outcomes, and broadening context), clinically hamstrung (professional deficiency, uncertainty and complexity in measurement, beyond medical purview, specificity of care, mechanistic mindset [focused on biochemical targets and comorbidities], avoiding alarm, and paradoxical dilemma), and undermined by system pressures (adhering to overarching policies, misalignment with mandates, and resource constraints). CONCLUSIONS: Improving patient-centered outcomes is regarded by nephrologists to encompass strategies that address patient goals and improve wellbeing and treatment burden in patients on hemodialysis. However, efforts are hampered by ambiguities about how to prioritize, measure, and manage the plethora of critical comorbidities and broader quality of life outcomes in a care setting that is technically demanding and driven by biochemical targets. Identifying critical patient-important outcomes and mechanisms for integrating them into practice may help to deliver patient-centered care in hemodialysis and other chronic disease settings.
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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 | 0.083 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".