Identifying Outcomes Important to Patients with Glomerular Disease and Their Caregivers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Shared decision making in patients with glomerular disease remains challenging because outcomes important to patients remain largely unknown. We aimed to identify and prioritize outcomes important to patients and caregivers and to describe reasons for their choices. DESIGN: We purposively sampled adult patients with glomerular disease and their caregivers from Australia, Hong Kong, the United Kingdom, and the United States. Participants identified, discussed, and ranked outcomes in focus groups using the nominal group technique; a relative importance score (between zero and one) was calculated. Qualitative data were analyzed thematically. RESULTS: Across 16 focus groups, 134 participants (range, 19-85 years old; 51% women), including 101 patients and 33 caregivers, identified 58 outcomes. The ten highest-ranked outcomes were kidney function (importance score of 0.42), mortality (0.29), need for dialysis or transplant (0.22), life participation (0.18), fatigue (0.17), anxiety (0.13), family impact (0.12), infection and immunity (0.12), ability to work (0.11), and BP (0.11). Three themes explained the reasons for these rankings: constraining day-to-day experience, impaired agency and control over health, and threats to future health and family. CONCLUSIONS: Patients with glomerular disease and their caregivers highly prioritize kidney health and survival, but they also prioritize life participation, fatigue, anxiety, and family impact.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".