Patient, clinician, and researcher prioritization of postoperative outcomes for people living with kidney failure: a modified Delphi process
Bibliographic record
Abstract
BACKGROUND: People with lived and living experience (PWLLE) of kidney failure have significantly high rates of surgery and postoperative complications. Despite these risks, there is lack of consensus on the most important postoperative outcomes for clinical care and research focused on this population. This study aimed to identify and compare the postoperative outcome priorities of PWLLE with those of clinicians/researchers. METHODS: We recruited PWLLE of kidney failure, clinicians and researchers from across Canada to complete a modified Delphi survey over three rounds between April and June 2024. Participants ranked 100 perioperative outcomes derived from literature, participant suggestions, and expert recommendations. Outcomes were evaluated for consensus using a 9-point Likert scale with predefined consensus criteria. Items not meeting consensus were either dropped or reconsidered in subsequent rounds. Median rankings of prioritized topics were compared between participant groups. RESULTS: Of 116 initial respondents, 91 consented to participate, with 64 (70%) completing all three rounds. Participants included 52 clinicians/researchers and 39 PWLLE. The top five prioritized postoperative outcomes were postoperative death within 30 days, intraoperative death, postoperative kidney allograft loss, septic shock, and 1-year postoperative mortality. Cardiovascular complications, dialysis dependency in individuals not requiring dialysis preoperatively, and risk of sepsis were also highly ranked. No significant differences were found in the top 10 priorities across participant groups (p > 0.05). CONCLUSION: We identified key postoperative outcomes for people with kidney failure undergoing surgery. This work will inform future research on perioperative care and the development of postoperative risk prediction tools for PWLLE of kidney failure. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.206 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".