Nephrologist Follow-Up versus Usual Care after an Acute Kidney Injury Hospitalization (FUSION): A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Survivors of AKI are at higher risk of CKD and death, but few patients see a nephrologist after hospital discharge. Our objectives during this 2-year vanguard phase trial were to determine the feasibility of randomizing survivors of AKI to early follow-up with a nephrologist or usual care, and to collect data on care processes and outcomes. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We performed a randomized controlled trial in patients hospitalized with Kidney Disease Improving Global Outcomes (KDIGO) stage 2-3 AKI at four hospitals in Toronto, Canada. We randomized patients to early nephrologist follow-up (standardized basket of care that emphasized BP control, cardiovascular risk reduction, and medication safety) or usual care from July 2015 to June 2017. Feasibility outcomes included the proportion of eligible patients enrolled, seen by a nephrologist, and followed to 1 year. The primary clinical outcome was a major adverse kidney event at 1 year, defined as death, maintenance dialysis, or incident/progressive CKD. RESULTS: =40). Nephrologist visits occurred in 24 of 34 (71%) intervention participants, compared with three of 37 (8%) participants randomized to usual care. The primary clinical outcome occurred in 15 of 34 (44%) patients in the nephrologist follow-up arm, and 16 of 37 (43%) patients in the usual care arm (relative risk, 1.02; 95% confidence interval, 0.60 to 1.73). CONCLUSIONS: Major adverse kidney events are common in AKI survivors, but we found the in-person model of follow-up posed a variety of barriers that was not acceptable to many patients. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: Nephrologist Follow-up versus Usual Care after an Acute Kidney Injury Hospitalization (FUSION), NCT02483039 CJASN 16: 1005-1014, 2021. doi: https://doi.org/10.2215/CJN.17331120.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".