Nephrologist’s Perceptions of Risk of Severe Chronic Kidney Disease and Outpatient Follow-up After Hospitalization With AKI: Multinational Randomized Survey Study
Bibliographic record
Abstract
Background: Patients hospitalized with acute kidney injury (AKI) have variable risks for chronic kidney disease (CKD); however, there is limited knowledge about how this risk influences outpatient follow-up with nephrologists. Objective: This survey study examined the likelihood that nephrologists would recommend outpatient follow-up of patients with varying risk profiles for CKD after hospitalization with AKI and the effect of reporting the predicted risk of severe CKD on their decision-making. Design: A randomized survey study examining the impact of providing predicted risks of severe CKD on nephrologists' follow-up recommendations for patients with AKI. Setting: The study included nephrologists from the United States, the United Kingdom, and Canada between September and December 2023. Patients: Participants reviewed clinical vignettes of patients with AKI and varying risks of severe CKD (G4 or G5), using an externally validated prediction model. Measurements: The primary outcome was the likelihood of recommending nephrologist specialist follow-up for each case, scored on a 7-point Likert scale (1 = "definitely not" and 7 = "definitely would"). Methods: Participants were randomized to receive a version of the survey either with or without the predicted risk of severe CKD included for each vignette. Responses were compared across categories of predicted risk (<10%, 10%-49%, and ≥50%) using generalized estimating equations. Results: Of the 203 nephrologists who participated, 73 (36%) were from the United Kingdom, 71 (35%) from Canada, and 45 (22%) from the United States. Mean (95% confidence interval [CI]) Likert scores increased from 4.01 (3.68, 4.34) for patients with a <10% predicted risk to 6.06 (5.76, 6.37) for those with a ≥ 50% predicted risk of severe CKD. Nephrologists were significantly less likely to recommend outpatient nephrology follow-up for patients with a <10% predicted risk of severe CKD when the risk was reported (mean difference = -0.71 [95% CI = -1.19, -0.23]), and significantly more likely to recommend follow-up for patients with a ≥50% predicted risk when the risk of severe CKD was reported (mean difference = 0.49 [95% CI = 0.04, 0.93]). Limitations: This study focuses on nephrologists from high-income countries and relies on hypothetical scenarios rather than real-world practices. Survey respondents may not be representative of all nephrologists, although consistent findings across diverse subgroups strengthen findings. Conclusions: When the predicted risk of severe CKD is reported, nephrologists are less likely to recommend follow-up for lower risk patients with AKI and more likely to recommend follow-up for higher risk patients, leading to better alignment of recommendations for outpatient follow-up with patient risk of severe CKD.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".